Input: What types of sensors are now capable of estimating physical activity levels and physiological outcomes of older adults?

Context:
\section{Introduction}
Cognitive deficit of older adults is one of the biggest global public health challenges in elderly care. Approximately 5.2 million people of 65 and older are suffered with any form of cognitive impairments in United States in 2012 \cite{stat12}. Dementia is one of the major causes of the cognitive impairments which is more acute among 85 and older population (50\%) \cite{stat12}. However, the costs (financial and time) of health care and long-term care for individuals with Alzheimer's (special form of dementia) or other dementias are substantial. For example, during 2016, about 15.9 million family and friends in United States provided 18.2 billion hours of unpaid assistance to those with cognitive impairments which is a contribution to the nation valued at \$230.1 billion. One the other hand, total payments for all individuals with all form of cognitive impairments are estimated at \$259 billion. Total annual payments for health care, long-term care and hospice care for people with Alzheimer's or other dementias are projected to increase from \$259 billion in 2017 to more than \$1.1 trillion in 2050. Among the above costs, a significant amount are relevant to clinical and diagnostic tests \cite{stat17}. Although clinical and diagnostic tests have become more precise in identifying dementia, studies have shown that there is a high degree of underrecognition especially in early detection. However, there are many advantages to obtaining an early and accurate diagnosis when cognitive symptoms are first noticed as the root cause findings of impairment always lessen the progress of impairment status and sometimes symptoms can be reversible and cured.

With the proliferation of emerging ubiquitous computing technologies, many stationary devices have been available to capture continuous functional and physiological behavior of older adults. Stationary sensors are now capable of estimating number of steps being taken, physical activity levels, sleep patterns and physiological outcomes (heart rate, skin conductance) of older adults. Ambient sensors also help capture the movement patterns of objects and humans for activity and behavior recognition \cite{dawadi14,dawadi15}. Researchers also proved the existence of correlations between cognitive impairment and everyday task performance \cite{dawadi14, akl15,alam16} as well as physiological symptoms \cite{alam16,sano15}. Although current studies showed some successes in IoT-assisted cognitive health assessment in different domains individually, there are several existing challenges in developing and validating a fully automated multi-modal assessment model.

\begin{enumerate}
\item \emph{Real-time IoT System}: A real-time IoT system must include a continuous and fault tolerant data streaming capability among central hub, wearable sensors and ambient sensors regardless of network communication protocol (WiFi, Ethernet, Bluetooth etc.) which are not available in existing researches.
\item \emph{Multi-modal Context Fusion}: Though several offline clinically validated cognitive health assessment tools exist \cite{wai03, starling99, krapp07, yesavage82, zung71}, there is no universally accepted method for IoT-assisted automatic cognitive health assessment in smart home environment that can fuse multi-modal sensor contexts altogether. For example, some researchers showed ambient sensors based Activities of Daily Livigin (ADLs) sequence pattern can signify the cognitive health status of older adults \cite{akl15, dawadi15}. Researchers also showed wearable Electrodermal Activity pattern analysis may carry the significance of cognitive status \cite{sano15}. However, for validation of IoT based cognitive health assessment, self-reported surveys, clinical diagnosis and observation based tools are used individually by prior researchers \cite{akl15, dawadi15, sano15, alam16}.
\end{enumerate}

Regarding aforementioned challenges for the automation of cognitive health assessment, \emph{AutoCogniSys} considers (i) reproducibility of our model in any smart home system consists of ambient motion sensors, wearable accelerometer (ACC) sensors, wearable Electrodermal Activity (EDA) and Photoplethysmography (PPG) sensors individually or combined streams; (ii) context awareness based on ambient motion sensors and wearable ACC sensors in any types of activities such as hand gestural, postural and complex ADLs; and (iii) high accuracy, i.e., a recall rate of over 90\% with less than 5\% false positive rate. More specifically, \emph{AutoCogniSys} extends our existing work \cite{alam16} in three dimensions,

\emph{(1) True Automation:} We first investigate the correlations of cognitive impairment with human activities and stress where we manually labeled activities, extract the corresponding physiological sensor (EDA and PPG) features of each activity, and use statistical method to find correlations. Then, we propose automatic complex activity recognition based on a Hierarchical Dynamic Bayesian Network (HDBN) model, fine-grained extraction of physiological sensor features and finally machine learning classification of cognitive impairment.

\emph{(2) Noises Elimination:} We define different types of noises on ACC, EDA and PPG sensors, propose extensive signal processing techniques to remove noises and show significant improvement can be achieved in cognitive impairment classification.

\emph{(3) Implementation and Evaluation:} Finally, we design and implement IoT system and analytic methods and minimize the human involvement to automate our proposed cognitive health assessment approach by considering effective smart home sensor customization and deployment, data collection, screening, cleaning and filtering, feature computation, normalization and classification, and activity  model training.

\textbf{Research Questions:} \emph{AutoCogniSys} consequently tackles the following key research questions.

$\bullet$ Can we detect simultaneously the periodic rhythms of  both hand gestures and postural activities from wrist-worn ACC sensor signal for diverse population (population with same activity but diverse ways such as walking with walker, stretcher or normally)? If so, how can we incorporate the hand gesture, posture and ambient sensor data streams to help improve the ADLs recognition models?

$\bullet$ How can we exploit and relate the micro-activity features into noise free physiological sensor signals processing to automate cognitive health assessment process? What are the critical roles of clinical survey and technology guided assessment methodologies and their inter-relationships for automating the different intermediate steps of cognitive health assessment process?

To tackle these, we make the following \textbf{key contributions}:

$\bullet$ We employ an extensive signal deconvolution technique that in conjunction with machine learning technique helps facilitate a wrist-worn ACC-based multi-label (hand gestural and postural) activity recognition for diverse population. We then leverage multi-label context sets with ambient and object sensor signals for complex activity recognition based on HDBN model.

$\bullet$ We propose a novel collaborative filter for EDA signal processing by postulating signal as a mixture of three components: \emph{tonic phase, phasic phase} and \emph{motion artifacts}, and employ convex optimization technique for filtering out the motion artifacts. We also propose a novel PPG signal processing technique to filter out the inherent motion artifacts and noises using improved Periodic Moving Average Filtering (PMAF) technique.

$\bullet$  We design and prototype an IoT system consisting of multiple devices (wearable wrist band, IP camera, object and ambient sensors) connected with central hub via WiFi, Ethernet and Bluetooth communication protocols. We collected data from 22 older adults living in a continuing care retirement community center in a very natural setting (IRB \#HP-00064387).

$\bullet$ Finally, we employ statistical and machine learning techniques to jointly correlate the activity performance metrics and stress (EDA and PPG) features that helps achieve max. 93\% of cognitive impairment status detection accuracy. We evaluate \emph{AutoCogniSys} on 5 clinically validated offline assessment tools as ground truth.
\section{Related Works}
\emph{AutoCogniSys} builds on previous works on wearable devices based low-level (postural and hand gestural) activity recognition and their integration with ambient sensors to recognize complex ADLs, the underlying signal processing and applications on cognitive health assessment automation.
\subsection{Wearable Sensor Signal Processing}
Wearable sensors can be two types: physical and physiological. Physical sensors (accelerometer, gyroscope etc.) signal values change over the movements of the sensor devices. Physiological sensors change over physiological condition of body such as EDA changes over stress and PPG changes over heart rate. However, physical movements also impose noises on physiological sensor signals which is called \emph{motion artifacts}.
\subsubsection{Physiological Signal Processing}
A continuous and descrete decomposition of EDA, and time and frequency domain analytics of PPG signal have been investigated before to extract relevant physiological features which were contaminated with noises and motion artifacts \cite{alam16}. \cite{setz10} denoised and classified EDA from cognitive load and stress with accuracy higher than 80\%. Though motion artifacts removal techniques such as exponential smoothing \cite{hern11} and low-pass filters \cite{poh10, hernandez14} provide significant improvement in filtering EDA signals, wavelet transforms offer more sophisticated refinement for any kind of physiological sensors such as electroencephalogram \cite{krish06, zikov02}, electrocardiogram \cite{erc06,alfa08}, and PPG \cite{lee03}. \cite{chen15} proposed a stationary wavelet transform (SWT) based motion artifacts removal technique. `cvxEDA' proposed  a convex optimization technique considering EDA as a mixture of white gaussian noise, tonic and phasic components where white gaussian noise includes motion artifacts and external noises \cite{greco16}. \emph{AutoCogniSys} intelligently combines SWT and `cvxEDA' together to remove noises and motion artifacts from EDA signal. On the other hand, it is more difficult to remove motion artifacts from PPG signal due to its periodicity of nature \cite{wang13}. Researchers proposed different methods such as frequency analytics \cite{garde13,wang13}, statistical analytics \cite{peng14} and digital filter \cite{lee10} to reduce noises and motion artifacts from PPG. \emph{AutoCogniSys} used Periodic Moving Average Filter (PMAF) in this regard \cite{lee07}.
\subsubsection{Physical Sensor Signal Processing}
ACC based hand gesture recognition has been explored by several researchers in past such as discrete hidden markov model \cite{liu10}, artificial neural network \cite{arce11}, weighted naive bayes and dynamic time warping \cite{mace13}. Akl et. al. proposed 18 gesture dictionary based Support Vector Machine (SVM) classifier \cite{akl11}. Wrist-worn ACC based postural activity recognition approach has been proposed using Decision Tree, Random Forest, Support Vector Machines, K-Nearest Neighbors, Naive Bayes and deep neural networks \cite{gj14, wang16}, the accuracy stagnates at 85\% using SVM method \cite{martin16}. However, neither of past works proposed any technique that can provide single body worn ACC sensor-based multiple body contexts recognition nor works efficiently for diverse posture say walking normally, with walker, with double walker or wheel chair. Our proposed 8-hand gesture recognition technique assisted sparse-deconvolution method improves classification performances on both normal and diverse postures. However, we incorporated hand gestures and postures in conjunction with ambient sensors into single-inhabitant HDBN model \cite{alam16b} that provides significant improvement in complex activity recognition.
\subsection{Cognitive Health Assessment}
Smart home environment has been used for providing automated health monitoring and assessment in the ageing population before \cite{dawadi14, gong15, akl15, dawadi15}. `SmartFABER' proposed a non-intrusive sensor network based continuous smart home environmental sensor data acquisition and a novel hybrid statistical and knowledge-based technique to analyz the data to estimate behavioral anomalies for early detection of mild-cognitively impairment \cite{riboni16}. \cite{skubic15} presented an example of unobtrusive, continuous monitoring system for the purpose of assessing early health changes to alert caregivers about the potential signs of health hazards. Though, prior researches proposed a sequence of ambient motion sensor streams as complex activity components in activity based health assessment \cite{dawadi14, gong15, akl15, dawadi15}, we consider inclusion of an wearable wrist-band with in-built ACC sensor to detect hand gesture and posture, augmenting with the ambient sensor readings to help recognize complex activities as well as cognitive health assessment of older adults. Additionally, we propose intelligent use of physiological features of skin through different physiological sensor signals (EDA, PPG) processing in daily activity tasks and incorporate context-awareness for automation of cognitive health assessment that have not been explored before.
\begin{figure}[!htb]
\begin{center}
   \epsfig{file=flowchart.pdf,height=1.6in, width=3.5in}
\caption{Overall flow of \emph{AutoCogniSys} pipeline.}
   \label{fig:overview}
\end{center}
\end{figure}
\section{Overall Architecture}
We first investigate existing IoT-based cognitive health care frameworks that covers every aspects of wearable (physical, physiological) and ambient (passive infrared and object) sensor signals computing. \emph{AutoCogniSys} is comprised of three component modules: (i)~sensing, (ii)~processing, and (iii)~analysis. The `sensing' module consists of clinical assessment tools (surveys, observation and clinical backgrounds) and sensing signals (ambient and wearable sensors). `Sensor processing' module is comprised of three sub-modules: a)~clinical assessment feature extraction from assessment tools; b)~ambient sensor feature extraction; and c)~wearable sensor processing (noise removal, segmentation, feature extraction, classification etc.). `Analysis' module is comprised of machine learning and statistical analytics-based score prediction of cognitive impairment. Automation of each module's functionality and inter-intra modular transactions without human interference can be called {\it true automation} of cognitive health assessment. Fig.~\ref{fig:overview} shows the overall flow of \emph{AutoCogniSys} which is discussed in details in the following sections.
\subsection{Demographic Ground Truth Data Collection}
Currently in standard clinical practice and research, the most accurate evaluations of cognitive health assessment are one-to-one observation and supervision tasks/questionnaires for monitoring an individual's functional abilities and behavior \cite{resnick15}. In the first stage of this pilot study, we have investigated current literatures and carefully chosen the clinically proven functional and behavioral health assessment survey tools \cite{resnick15}. On the other hand, to cross check with the survey based evaluations, we have also chosen clinically justified observation based behavioral assessment methods. First, following the resident consent, our clinical research evaluator collects demographic and descriptive data (age, gender, race, ethnicity, marital status, education and medical commodities). She has performed two types of clinical assessments: (1) \emph{Observation based} where the resident's cognition is assessed using the Saint Louis University Mental Status (SLUMS) scale \cite{wai03}. (2) \emph{Survey based} where five widely used and clinically well validated surveys are taken into account: (a) \emph{Yale Physical Activity Survey} \cite{starling99}; (b) \emph{Lawton Instrumental Activities of Daily Living}; (c) \emph{Barthel Index of Activities of Daily Living} \cite{krapp07}; (d) \emph{Geriatric Depression Rating scale} \cite{yesavage82}; and (e) \emph{Zung Self-Rating Anxiety scale} \cite{zung71}.
\subsection{Smart Environment Creation}
For an ideal IoT-based system, instrumenting and deploying it at each participant's natural living environment warrants for assembling a flexible set of hardware and software interfaces to ease the system configuration, setup, and network discovery processes. The sensor system placed in the residences of volunteers needs to meet several specific physiological signals and activity monitoring needs. However, we must confirm that the devices are reliable with potential for re-deployment as well as appear unintimidating to the participants. Inspired by the above requirements, we developed a real testbed IoT system, {\it SenseBox}, by customizing Cloud Engine PogoPlug Mobile base station firmware to integrate with WiFi (connect ambient and object sensors) and Bluetooth (connect wristband) protocol. The smart home components are as follows: (i) PogoPlug base server with a continuous power supply, (ii) 3 binary Passive Infrared sensors in three different rooms (kitchen, livingroom and bedroom) to capture room level occupancy, (iii) 7 binary object sensors attached with closet door, entry door, telephone, broom, laundry basket, trash can and trash box, (iv) three IP cameras in the appropriate positions to collect the ground truth data and (v) an Empatica E4 \cite{empatica} wrist-band (integrated sensors: PPG at 64 Hz, EDA at 4 Hz, Body temperature at 1 Hz and a triaxial ACC at 32 Hz) on the participant's dominating hand.
\section{Activity Recognition}
We aim to detect single wrist-worn ACC sensor based hand gesture and postural activities and insert these into an HDBN graphical model in conjunction with ambient and object sensor values for complex activity recognition. We consider the recognition problem asan activity tupple of $\langle gesture,posture,ambient,object \rangle$. Though, Alam et. al. provides significant performance improvement for single wrist-worn ACC sensor aided 18-hand gesture based postural activity recognition in lab environment \cite{alam17}, it faces some practical challenges in real-time smart environment with older adults due to the diversity of their postures. For example, some older adults use walker, double walking sticks or wheel chair for walking in which cases collecting 18 hand gestures and corresponding postural activities for training requires endless efforts and carefulness. To reduce the complexity of ground truth labeling and later state space explosion for graphical model (HDBN), we propose to use rotational normalization method that can merge some hand-gestures subject to directional differences and forms an 8-hand gesture model. However, our proposed Feature Weight Naive Bayes (FWNB) classifier adds significant improvement on Alam et. al. proposed sparse-deconvolution method as well as recognition in diverse postural environment.
\begin{figure}[!htb]
\begin{center}
   \epsfig{file=hand_gestures.pdf,height=0.5in, width=3in}
   \vspace{-.2in}
\caption{8 hand gesture dictionary with direction}
   \label{fig:hand_gestures}
   \vspace{-.2in}
\end{center}
\end{figure}
\subsection{Hand Gesture Recognition}
\label{sec:hand_gesture}
\emph{AutoCogniSys} proposes an 8-gesture dictionary (as shown in Fig. \ref{fig:hand_gestures}) and a Feature Weighted Naive Bayesian (FWNB) framework for building, modeling and recognizing hand gestures. The method comprises of the following steps: (i) \emph{Preprocessing:} wrist-worn ACC sensor provided 3-axis data are passed through 0.4Hz low-pass filter to remove the data drift. (ii) \emph{Rotation normalization:} Normalizing the rotation of hand gestures provides greater accuracy and allows for more realistic, orientation-independent motion. At first, we find the best fit plane of the acceleration vectors thus if the motion lies in a single plane, then the acceleration vectors of a closed shape should on average lie in that main plane. Then, we take all acceleration segments between points of inflection to form one single vector called reference vector that provides us the general direction of user's motion. After that, each vector is normalized relative to the reference vector. This normalization helps remove a lot of hand gestures from prior considered 18 hand gestures resulting a reduced dictionary of 8 gestures. (iii) \emph{Feature Weighted Naive Bayesian model:} Naive Bayes classifier is light-weight and efficient technique for hand gesture recognition. We extract 12 ACC features \cite{alam17} and calculate weight for each feature type based on the similarity of feature measures of the trained gestures (0$<$weight$<$1). While recognizing gestures, the proximity of each feature measure to the average trained feature measure of each gesture type is calculated by a normal distribution. Then, the proximity value is multiplied by the feature weight that was calculated in the training phase.  All of these multiplied values are added together and the system predicts the gesture type with the greatest value as the user gesture. In the learning data points, there should be static postural activities (such as sitting, lying etc.) to avoid unexpected noises over wrist-worn ACC sensors. In the final hand gesture dictionary, we save the reference vector as our signal dictionary.
\subsection{Postural Activity Recognition}
In normal lab environment, wrist-worn ACC sensor signal is a mixture (convolution) of actual hand gesture and postural activity relevant signals \cite{alam17}. \emph{AutoCogniSys} improves the idea by reducing the number of hand gestures and postural activities to 8 (as shown in Fig.\ref{fig:hand_gestures}) using rotation normalization and 4 (walking, sitting, standing and lying). Then, we use sparse-deconvolution method (with 31\% signal reconstruction error) to get Approximately Sparse Factor. The summary of the entire process is stated bellow:

{\it Building Deconvolution Method:} We first consider the wrist-worn ACC sensor signals (3-axis values) as a convolution of hand gesture and postural activity effects and build a deconvolution framework. The deconvolution framework takes a known signal (hand gesture effects) and a equalizer parameter ($\lambda$) as input and provides an Approximately Sparse Factor signal (postural activity effects) as output. For 3-axis ACC signals, we need to learn associated 3 equalizer parameters for each hand gesture. Moreover, each equalizer parameter is involved with 4 postural activities that results a total 96 ($8\times 3\times 4$) equalizer parameters to learn.

{\it Learning Classification Model:} We use the Approximately Sparse Factor signal to extract 12 statistical features and SVM with sequential machine optimization (SMO) \cite{cao06} for postural activity recognition.

{\it Prediction Model:} After recognizing the hand gestures following the method explained in Sec.~\ref{sec:hand_gesture}, we take the corresponding reference vector as known signal and extract the Approximately Sparse Factor signals incorporating corresponding 3 equalizer parameters ($\lambda$) for the sparse-deconvolution method. Then, we apply feature extraction and prior learned SMO based SVM classifier \cite{cao06} to classify final postural activity. Fig.~\ref{fig:deconvolution} illustrates a single axis example of the deconvolution.

\begin{figure}[!htb]
\begin{center}

   \epsfig{file=deconvolution.pdf,height=1.6in, width=3in}
   \vspace{-.15in}
\caption{Sample deconvolution example of X-axis. The raw x-axis of accelerometer signal, reference vector of the sample gesture and the extracted corresponding ASF signal of walking.}
   \label{fig:deconvolution}
\end{center}
\vspace{-.15in}
\end{figure}

\subsection{Complex Activity Recognition}
We build a HDBN based complex activity recognition framework for single inhabitant scenario smart home environment \cite{alam16b} taking the advantage of detected hand gestural and postural activities along with the ambient and object sensor streams. At first, we obtain instant hand gestural and postural activities from our above proposed models, and additionally motion sensor and object sensor readings from our IoT-system for every time instant generating a 4-hierarchy of HDBN model. Considering the context set $\langle gestural, postural, ambient,object\rangle$ as a hierarchical activity structure (extending two 2-hierarchical HDBN \cite{alam16b}), we build complex activity recognition model for single inhabitant scenario. Finally, we infer the most-likely sequence of complex activities (and their time boundaries), utilizing the well-known Expectation Maximization (EM) algorithm \cite{dempster77} for training and the Viterbi algorithm \cite{forney73} for run-time inference.
\section{Automatic Activity Features Estimation}
The effects of cognitive ability on daily activity performance have been studied before \cite{dawadi14,akl15}. They experimentally and clinically validated that cognitive impairment highly reduces the daily activity performances and this activity performance can be computed as an indicator of cognitive ability status of older adults. The standard activity features refer to completeness of task (TC), sequential task ability (SEQ), interruption avoidance capabilities (INT) etc. In current behavioral science literature, the above activity features carry specific definition based on the sub-tasks involved with a complex activity \cite{dawadi14,akl15}. Completeness of task refers to how many sub-tasks are missed by the participants. Sequential task ability refers to how many sequences of sub-tasks are missed referring the gerontologist defined standard sequences of the sub-task for the particular complex activity. Interruption avoidance capability refers to how many times the participants stop or interleave while doing any sub-task. The final goal of activity features estimation is to provide overall task score. The task score is proportional to the functional ability of participants in performance daily activities. Our behavioral scientist team, comprises with Nursing professor, gerontologist and retirement community caregivers, carefully discus, optimize and choose 87 sub-tasks in total for 13 complex activities.

Each of the sub-task comprises with sequential occurrences of hand gesture and postural activities. However, no researchers ever considered hand gesture for activity features estimation due to complexity of multi-modal wearable and ambient sensors synchronization and multi-label activity classification \cite{dawadi14,akl15}. \emph{AutoCogniSys} exploited single wrist-worn sensor based hand gesture and postural activity recognition, and proposed an activity features (TC, SEQ and INT) estimation method including these two parameters in conjunction with object and ambient sensor features that provide significant improvement of cognitive health assessment of older adults.
\subsection{Machine Learning Based Complex Activity Features Estimation}
In current cognitive health assessment literature, complex activity features can be defined as $\langle TC,SEQ,INT,TS\rangle$. We used supervised method to estimate TC, SEQ and INT, and unsupervised method to estimate TS. We first, formulate the automated scoring as a supervised  machine learning problem in which machine learning algorithms learn a function that maps $\langle${\it hand gesture, posture, object, ambient sensor}$\rangle$ feature set to the direct observation scores. We use bagging ensemble method to learn the mapping function and SMO based SVM \cite{cao06} as base classifier. The learner averages by boostrapping individual numeric predictions to combine the base classifier predictions and generates an output for each data point that corresponds to the highest-probability label. We train three classifiers considering observation as ground truth for TC, SEQ and INT scores and test on the testing dataset. We derive unsupervised scores using dimensionality reduction technique for each feature set. First, we take all features of each activity, apply optimal discriminant analysis technique as a dimensionality reduction process \cite{zhang09} and reduce the feature sets into single dimensional value which represents the automated task completeness scores of the particular user activity. A min-max normalization is applied that provides us a uniform range of the variables using $
z_i=\frac{x_i-min(x)}{max(x)-min(x)}$ equation where $x=\{x1,\ldots,x_n\}$ and $z_i$ is $i^{th}$ normalized data. The final single dimensional score represents machine learning based TS score.
\section{Physiological Sensor Signals Processing}
The autonomic nervous system (ANS) restrains the body's physiological activities including the heart rate, skin gland secretion, blood pressure, and respiration. The ANS is divided into sympathetic (SNS) and parasympathetic (PNS) branches. While SNS actuates the body's resources for action under arousal conditions, PNS attenuates the body to help regain the steady state. Mental arousal (say stress, anxiety etc.) activates the sweat gland causing the increment and reduction of  Skin Conductance on SNS and PNS physiological conditions respectively. However, Instant Heart Rate also has similar effect on SNS and PNS physiological condtions i.e., a higher value of heart rate is the effect of SNS and lower value is the outcome of PNS. EDA and PPG sensors are widely used to estimate the instant value of skin conductance and heart rate respectively \cite{alam16}.
\subsection{EDA Sensor Signal Processing}
EDA is the property of the human body that causes continuous variation in the electrical characteristics of the skin which varies with the state of sweat glands in the skin. There are three types of arousal: \emph{cognitive, affective and physical}. \emph{Cognitive} arousal occurs when a person tries to solve any problem using her cognitive ability. \emph{Affective} arousal occurs when a person is worried, frightened or angry either doing daily activities or in resting position. On the other hand, \emph{physical} arousal is related to the brain command to move bodily parts which is imposed on the total arousal as an artifact, called \emph{motion artifact}. However, there are always some noises due to the weather conditions (temperature, humidity etc.) and device motion. This \emph{motion artifact} can be the prime cause of signal contamination of physiological outcomes while performing daily activities which must be removed. \emph{AutoCogniSys} proposes an EDA sensor signal processing method consists of three steps: (i) noise and motion artifacts removal, (ii) separation of tonic component and phasic component (explained later) from contamination free EDA signal and (iii) feature extraction on the response window.
\subsubsection{Motion Artifacts Removal}
There are many types of motion artifacts but the unsual steep rise is the mostly occured ones associated with EDA signal while performing daily activities \cite{edel67}. We use well-known steep rising noises reduction technique, SWT \cite{chen15}. We first consider EDA signal as a mixture of a slow variant tonic and fast variant phasic component, i.e., SWT coefficient is modeled as a mixture of two Gaussian components, phasic (close to zero valued signal) and tonic (high rising signal). After expanding EDA signal into multiple levels of scaling and wavelet coefficients, we choose adaptively a threshold limit at each level based on the statistical estimation of the wavelet coefficients' distribution, and employ that on the wavelet coefficients of all levels. Finally, an inverse wavelet transform technique is applied to the thresholded wavelet coefficients to obtain the artifacts free EDA signal. Fig~.\ref{fig:eda_artifact_removal} shows a sample of raw and motion artifacts free EDA signal.

\begin{figure}[!htb]
\begin{center}
\vspace{-.1in}
   \epsfig{file=eda_signal_artifact.pdf,height=1.6in, width=3.5in}
\caption{Dashed line represents noisy EDA signal and solid red line represents \emph{AutoCogniSys} proposed motion artifact free EDA signal}
   \label{fig:eda_artifact_removal}
\end{center}
\end{figure}
\subsubsection{Convex Optimization Technique to EDA Deconvolution}
After the motion artifact removal, we consider EDA as the sum of three components for $N$ sample: a slow tonic driver ($t$), fast (compact, bursty) non-negative sparse phasic driver ($r$) and a reminder error term ($\epsilon_r$).
\begin{equation}
\label{eq:eda_signal}
y = t + r + \epsilon_r
\end{equation}
This additive error $\epsilon_r$ is a White Gaussian Noise. The central problem associated with the deconvolution method is to get tonic $t$ component from the above equation. \cite{greco16} showed that EDA signal deconvolution (separation of tonic, phasic and noise terms from EDA signal) is a quadratic optimization problem and defined tonic component as follows:
\begin{equation}
\label{eq:tonic}
t = Bl + Cd,
\end{equation}
where $B$ is a tall matrix whose columns are cubic $B$-spline basis functions, $l$ is the vector of spline coefficients, $C$ is a $N\times 2$ matrix, $d$ is a $2\times 1$ vector with the offset and slope coefficients for the linear trend. The above equation is subject to the following optimization problem,
\begin{eqnarray}
minimize \frac{1}{2} {||Mq + Bl + Cd- y||}^2_2 +\alpha {||Aq||}_1 + \frac{\lambda}{2} {||l||}^2_2\\
subject\;to\; Aq \geq 0\nonumber
\end{eqnarray}
where $M$ and $A$ are tridiagonal matrices and $q$ is an auxiliary variable. After solving the above equation, we can get the optimal values for $\{q,l,d\}$ that can be used to obtain tonic component from the equation~\ref{eq:tonic}. The reminder of the equation~\ref{eq:eda_signal} ($r+\epsilon_r$) is considered as a mixture of White Gaussian Noise ($\epsilon_r$) and a fast variant phasic component ($r$). We employ butterworth low-pass filter (5Hz) and hanning smoothing with window size 4 (optimal) to remove $\epsilon_r$ from phasic component ($r$).
\subsection{PPG Signal Processing}
PPG is used mainly for measuring the oxygen saturation in the blood and blood volume changes in skin. An ideal PPG signal processing must contain the following steps: noise and motion artifacts removal, heart rate detection, heart rate variability estimation and feature extraction.
\subsubsection{PPG Signal Noise and Motion Artifacts Removal}
Similar to EDA signal, PPG signal is also contaminated with motion artifacts and noises. However, unlike EDA signal, PPG produce quasiperiodicity in a time series spectrum \cite{mete30}. We use Periodic Moving Average Filter (PMAF) to remove motion artifacts and noises \cite{lee07}. We first segment the PPG signal on periodic boundaries and then average the $m^{th}$ samples of each period. After filtering the input PPG signal with a 5-Hz $8^{th}$-order Butterworth low-pass filter, we estimate the maximum and minimum value of each period. The mean of each period are obtained from the maximum and minimum values applying the zero crossing method. These points of the means help determine the boundaries of each period. Then, interpolation or decimation is performed to ensure that each period had the same number of samples \cite{lee07}.
\subsubsection{Heart Rate and Heart Rate Variability Estimation}
We first apply PMAF on PPG signal to remove noises and motion artifacts, refine PPG by smoothing the signal using 1-dimensional Gaussian Filter and Convolution, calculate first derivative of the convoluted signal and finally find the differences between two consecutive peak values which is called HRV \cite{sel08}. The occurrences of total peak values (R-peak or beat) in each minute is called Heart Rate (HR) with an unit of Beat Per Minute. The signal value property of HRV and HR are inversely proportional which means the mental arousal that increases HR should decrease HRV in the time segment window. Fig~\ref{fig:ppg_artifact_removal} shows a sample of the noisy and filtered PPG signal and their corresponding Instant Heart Rate.
\begin{figure}[!htb]
\vspace{-.1in}
\begin{center}
   \epsfig{file=ppg_artifact_removal.pdf,height=1.4in, width=3.5in}
   \vspace{-.15in}
\caption{Top figure illustrates the noisy signal (dotted line) and filtered signal from PPG sensor based on our filtering method. Bottom figure illustrates instant heart rate calculated from noisy signal (dotted line) and filtered signal}
   \label{fig:ppg_artifact_removal}
\end{center}
\vspace{-.15in}
\end{figure}
\subsection{Physiological Sensor Signal Feature Extraction}
Using the above mentioned methods, we removed the noises and motion artifacts from EDA and PPG signals and generated two time series signal from EDA (tonic and phasic components) and one time series signal from PPG (HRV). Then, we segment each of the time series signal based on our prior detected complex activities such that each response window starts and ends with the starting and ending points of each complex activity. We extract 7 statistical time-series features for EDA (as shown in Table~\ref{tab:eda_features}) and 8 features for HRV (Table~\ref{tab:hrv_features}) within the response window).

\begin{table}[!t]
\begin{center}

\renewcommand{\arraystretch}{1}
\caption{EDA Features Within The Response Window}
\begin{scriptsize}

\label{tab:eda_features}
\begin{tabular}{|c|l|}
\hline
\bfseries Features& \bfseries Description\\
\hline
nSCR & Number of SCRs within response window (wrw)\\
\hline
Latency & Response latency of first significant SCR wrw\\
\hline
AmpSum & Sum of SCR-amplitudes of significant SCRs wrw\\
\hline
SCR & Average phasic driver wrw\\
\hline
ISCR & Area (i.e. time integral) of phasic driver wrw\\
\hline
PhasicMax & Maximum value of phasic activity wrw\\
\hline
Tonic & Mean tonic activity wrw\\
\hline
\end{tabular}
\end{scriptsize}
\end{center}
\end{table}

\begin{table}[!t]
  \begin{center}
\renewcommand{\arraystretch}{1}
\vspace{-.3in}
\caption{Heart Rate Variability features}
\label{tab:hrv_features}
\begin{scriptsize}
\begin{tabular}{|c|l|}

\hline
\bfseries Feature& \bfseries Description\\
\hline
$\overline{RR}$&Mean RR intervals\\
\hline
SDNN&Standard deviation of RR intervals\\
\hline
SDSD&Std of successive RR interval differences\\
\hline
RMSSD&Root mean square of successive differences\\
\hline
NN50&\#successive intervals differing more than 50 ms\\
\hline
pNN50&relative amount of NN50\\
\hline
HRVTI&Total number of RR intervals/height of the histogram\\
\hline
TINN&Width of RR histogram through triangular interpolation\\
\hline
\end{tabular}
\end{scriptsize}
  \end{center}
\end{table}
\section{Experimental Evaluation}
In this section, we explain our data collection, available benchmark dataset, baseline methods and evaluation.
\subsection{Datasets and Baseline Methods}
We validate and compare \emph{AutoCogniSys} with baseline methods on both publicly available and our collected datasets.
\subsubsection{RCC Dataset: Collection and Ground Truth Annotation}
For collecting Retirement Community Center Dataset (RCC Dataset), we recruited 22 participants (19 females and 3 males) with age range from 77-93 (mean 85.5, std 3.92) in a continuing care retirement community with the appropriate institutional IRB approval and signed consent. The gender diversity in the recruited participants reflects the gender distribution (85\% female and 15\% male) in the retirement community facility. A trained gerontology graduate student evaluator completes surveys with participants to fill out the surveys. Participants are given a wrist band to wear on their dominant hand, and concurrently another trained IT graduate student have the IoT system setup in participants' own living environment (setup time 15-30 minutes). The participants are instructed to perform 13 \emph{complex ADLs}. Another project member remotely monitors the sensor readings, videos and system failure status. The entire session lasts from 2-4 hours of time depending on participants' physical and cognitive ability.

We follow the standard protocol to annotate demographics and activities mentioned in the IRB. Two graduate students are engaged to annotate activities (postural, gestural and complex activity) whereas the observed activity performances are computed by the evaluator. Two more graduate students are engaged to validate the annotations on the videos. In overall, we are able to annotate 13 complex activities (total 291 samples) labeling for each participant; 8 hand gestures (total 43561 samples) and 4 postural activities (total 43561 samples) labeling. Annotation of postural and complex activities outcomes no difficulties from recorded videos. However, annotation of hand-gestures is extremely difficult in our scenario. We used video based hand tracker that can track and sketch wrist movements from a video episode \cite{hugo14}. This sketching can help us significantly to identify which particular hand gesture is being performed in the time segment.
\subsubsection{EES Datasets: EDA and PPG Sensor Datasets}
We used Eight-Emotion Sentics (EES) dataset to validate \emph{AutoCogniSys} proposed physiological signal processing approaches \cite{picard01}. The dataset consists of measurements of four physiological signals (PPG/Blood Volume Pulse, electromyogram, respiration and Skin Conductance/EDA) and eight affective states (neutral, anger, hate, grief, love, romantic love, joy, and reverence). The study was taken once a day in a session lasting around 25 minutes for 20 days of recordings from an individual participant. We consider only PPG and EDA for all of the affective states in our study.
\subsubsection{Baseline Methods}
Though no frameworks ever combined all modalities together into real-time automated cognitive health assessment, we evaluate \emph{AutoCogniSys} performance by comparing the performances of its components individually with upto date relevant works. For hand gesture and postural activity recognition, we consider \cite{alam17} proposed method as baseline. For complex activity recognition, we compare our hand gesture and postural activity classifiers aided HDBN model with three-level Dynamic Bayesian Network \cite{zhu12} framework. For activity performance estimation, activity performance based cognitive health assessment; and EDA and PPG based cognitive health assessment, we have considered \cite{alam16} proposed method as baseline.
\subsection{Activity Recognition Evaluation}
The standard definition for \emph{accuracy} in any classification problem is $\frac{TP+TN}{TP+TN+FP+FN}$ where $TP,TN,FP$ and $FN$ are defined as true positive, true negative, false positive and false negative. For complex activity recognition evaluation, we additionally consider \emph{start/end duration error} as performance metric that can be explained as follows: consider that the true duration of ``cooking'' is 30 minutes (10:05 AM - 10:35 AM) and our algorithm predicts 29 minutes (10.10 - to 10.39 AM). Then, the start/end duration error is 9 minutes ($|$5 minutes delayed start$|$ + $|$4 minutes hastened end$|$), in an overall error of e.g., 30\% (9/30=0.3). We measure cross-participant accuracy using leave-two-participants-out method for performance metrics, i.e., we take out two of the participants' data points from the entire dataset, train our proposed classification models, test the model accuracy on the two left-out participants relevant data points, and continue the process for entire dataset.

\begin{figure*}[!htb]
\begin{minipage}{0.45\textwidth}
\begin{center}
   \epsfig{file=hand_gesture_accuracy.pdf,height=1.6in, width=3in}
\caption{Feature Weighted Naive Bayes (FWNB) classification accuracy comparisons with baseline approaches (graphical signatures of all hand gestures are shown).}
   \label{fig:hand_gesture_accuracy}
\end{center}
\end{minipage}
\begin{minipage}{0.29\textwidth}
\begin{center}
\vspace{-.12in}
   \epsfig{file=posture_accuracy_normal.pdf,height=1.6in, width=2.1in}
\caption{4-class postural level activity recognition performance and comparisons with baseline method}
   \label{fig:posture_accuracy_normal}
\end{center}
\end{minipage}
\begin{minipage}{0.25\textwidth}
 \begin{center}
 \vspace{-.12in}
   \epsfig{file=posture_accuracy_extended.pdf,height=1.6in, width=2.1in}
\caption{6-class diverse postural activity recognition framework accuracy comparisons with the baseline approach.}
   \label{fig:posture_accuracy_extended}
\end{center}
 \end{minipage}
\end{figure*}

Fig~\ref{fig:hand_gesture_accuracy} displays Feature Weighted Naive Bayes (FWNB) based the 8-hand gestural activity recognition accuracies comparisons with the baseline methods which clearly depicts the outperformance of our method (5\% improvement) with an overall accuracy of 92\% (FP rate 6.7\%) in RCC dataset. For postural activity recognition, dataset achieving 91\% postural activity recognition accuracy (FP rate 9.5\%) which outperforms the baseline approach significantly (8\% improvement). Now, we expand the postural activities for RCC datasets into 3 diverse `walking' postures: `normal walking', `walking with walker', `walking with single stick' and the accuracy goes down to 88\% (FP 7.9\%). Fig.~\ref{fig:posture_accuracy_normal} and Fig.~\ref{fig:posture_accuracy_extended} illustrate 4-class postural and extended 6-class postural classifier accuracies respectively which clearly posit that \emph{AutoCogniSys} outperforms in each case of postural activities as well as overall performances (8\% and 7\% improvement respectively).

For complex activity classification, we choose RCC dataset to train our HDBN model. Our leave-two-participants out method results an accuracy of 85\% (FP Rate 3.6\%, precision 84.2\%, recall 84.5\%, ROC Area 98.2\%) with a start/end duration error of 9.7\%. We run the entire evaluation for baseline complex activity recognition algorithm too achieving an overall accuracy of 78\% (FP Rate 5.2\%, precision 79.6\%, recall 78.5\%, ROC Area 82.7\%) which is clearly lower performed method than our approach. Fig. \ref{fig:complex_activity_roc} and Fig~\ref{fig:complex_activity_accuracy} illustrate the ROC curve and each complex activity recognition accuracy comparisons with baseline method which depict the outperformance of our framework over baseline methods (7\% improvement). Fig~\ref{fig:complex_activity_accuracy} also shows that inclusion of postural activity improves the final complex activity recognition (4\% improvement).
 \begin{figure}  [!htb]
  \begin{minipage}{0.15\textwidth}
 \begin{center}
   \epsfig{file=complex_activity_roc.pdf,height=1.4in, width=1.1in}
\caption{ROC curve for complex activity recognition}
   \label{fig:complex_activity_roc}
\end{center}
\end{minipage}
\begin{minipage}{0.33\textwidth}
\begin{center}

   \epsfig{file=complex_activity_accuracy.pdf,height=1.4in, width=2.3in}
\caption{Complex ADLs recognition accuracy improvement and comparison with baseline \cite{zhu12} and HMM based method}
   \label{fig:complex_activity_accuracy}
\end{center}

\end{minipage}
\end{figure}

\subsection{Quantification of Performance Score}
To characterize both the qualitative and quantitative health assessment performance scores, we start with four different feature groups ranging from both functional and physiological health measures: (i) observation based activity features, (ii) automatic activity performance features, (iii) EDA features and (iv) PPG features.

In \emph{observation based activity features}, we design a complex activity set comprised of multiple subtasks which are involved with task {\it interruption, completion and sequencing}. Participants are instructed to perform the complex activities while the trained evaluator observed the aforementioned functional activity performance measures. Each incorrect attempt of performance measure will be assigned one point thus higher score reflects lower performance of functional activities \cite{dawadi14}. We first detect hand gesture and postural activities. Then, we feed the low-level activity contexts (gestural and postural) combined with ambient contexts (object and ambient motion sensor readings) into HDBN for single inhabitant model \cite{alam16b} to recognize complex activities. The complex activity recognition framework provides both activity labels and activity window (start-end points). Then, we extract features of object sensor, ambient sensor, gestural activity and postural activity events for each activity window. The features are number of occurrences, mean number of occurrences, consecutive 1, 2, 3, $\ldots$ 20 occurrences, top 10, 20, 30, $\ldots$, 90 percentile etc (29 features in total). In \emph{physiological features} we first detect 13 complex activities using HDBN algorithm which provides activity labels and activity window (start-end points), apply noise reduction, motion artifacts removal, extract 7 EDA features and 8 HRV features for each activity and take the mean of them over time (minutes) to get 15 (7+8) complex activity physiological features set for each participant. In summary, we extract 3 observation based activity features, 29 automatic activity performance features, 7 EDA features and 8 HRV features.
\subsection{Physiological Signal Processing Performance Evaluation}
Standard evaluation technique should use both experimental and publicly available datasets to confirm the outperformance of the novel approaches. We first evaluate our physiological signal processing techniques using a publicly available dataset (EES Dataset \cite{picard01}) to detect 8 human emotions. Then, in next section, we evaluate our methods in assessing cognitive health status of older adults using RCC dataset.

For EDA, we first apply SWT method to remove motion artifacts and noises. Then, we use cvxEDA method to separate tonic and phasic components of EDA. Then, we extract 7 EDA features on a sliding window of 4 seconds. Finally, we feed the 7 EDA features into a SMO based SVM algorithm \cite{cao06}. We use 10-fold cross validation to classify eight emotions achieving 87\% of overall accuracy (FP rate 6\%). For PPG, we first apply our proposed PMAF based noises and motion artifacts removal technique. Then, we calculate HRV and perform time-domain feature extraction to extract 8 HRV features on a sliding window of 4 seconds. We feed these features into a SMO based SVM algorithm \cite{cao06}. Our 10-fold cross validation shows accuracy of 79\% (FP rate 11.5\%) of detecting 8 emotions on EES Dataset. Fig. \ref{fig:ees_eda} and Fig. \ref{fig:ees_ppg} clearly depict that \emph{AutoCogniSys} proposed EDA and PPG signal processing techniques significantly improve the accuracy over the baseline \cite{alam16} method (10\% and 12\% improvement).

\begin{figure}[!htb]
\begin{minipage}{0.24\textwidth}
\begin{center}
   \epsfig{file=ees_eda.pdf,height=1.2in, width=1.8in}
\caption{(EES Databaset) EDA features based Eight Emotion classification accuracy comparisons with baseline method}
   \label{fig:ees_eda}
\end{center}
\end{minipage}
\begin{minipage}{0.23\textwidth}
\begin{center}
   \epsfig{file=ees_ppg.pdf,height=1.2in, width=1.7in}
\caption{(EES Dataset) PPG features based 8-Emotion classification accuracy comparisons with baseline method}
   \label{fig:ees_ppg}
\end{center}
\end{minipage}

\end{figure}
\subsection{Evaluation of Performance Scores}
The feature subsets used in the experimentation for observation and survey based clinical assessments and technology guided physiological and activity initiated health assessments are depicted in Table~\ref{tab:feature_subset}. From our 6 demographics surveys, we find significant distributions in terms of cognition only for SLUMS Score (S-Score). Based on that, we divide our participants pool into three groups: \emph{Not Cognitively Impaired (NCI), Mild Cognitively Impaired (MCI) and Cognitively Impaired (CI)} where the number of participants are $5$, $7$ and $10$ respectively.
\begin{table}[!t]
\begin{scriptsize}

{\centering
\renewcommand{\arraystretch}{.6}
\caption{Feature Subsets}
\label{tab:feature_subset}
\begin{tabular}{|l|L{5.5cm}|}
\hline
\bfseries Feature& \bfseries Description\\
\hline
Observation & Task Completeness (TC), Sequencing (SEQ), Interruptions (INT)\\
\hline
Survey & SLUMS Score (S-Score), ZUNG Score (Z-Score), IADL Score (I-Score), Yale Score (YPAS), Barthel Score (B-Score), GDS Score (G-Score)\\
\hline
EDA  and HRV & 7 and 8 Features\\
\hline
Activity Performance& Supervised (TC, SEQ, INT), Unsupervised\\
\hline
Arousal& EDA and HRV features of each complex activity window\\
\hline

\end{tabular}
}
\end{scriptsize}
\end{table}

\begin{figure}[!htb]
\begin{center}
   \epsfig{file=group_correlation.pdf,height=1in, width=3.3in}
\caption{\emph{AutoCogniSys} Proposed Method Based Group Correlation analysis ( $r-value$) NCI, MCI and CI represent not cognitive, mild cognitive and cognitively impaired group of population. TC, INT, SEQ, EDA and HRV represent task completeness, interruption scores, sequencing scores, electrodermal activity features and heart rate variability features}
   \label{fig:group_correlation}
\end{center}
\vspace{-.2in}
\end{figure}
\begin{figure}[!htb]
\begin{center}
   \epsfig{file=group_correlation_baseline.pdf,height=1in, width=3.3in}
\caption{Baseline \cite{alam16} method based Group Correlation analysis ( $r-value$)}
   \label{fig:group_correlation_baseline}
   \vspace{-.25in}
\end{center}
\end{figure}

\subsection{Statistical Correlation Analysis of Cognitive Health}
We used Pearson correlation coefficients with significance on $p<0.05$* for individual feature and partial correlation coefficients with significance on $p<0.005$** for group of features correlation analysis. Fig. \ref{fig:group_correlation} and Fig. \ref{fig:group_correlation_baseline} show the group correlation analysis results based on \emph{AutoCogniSys} proposed framework and baseline \cite{alam16} framework respectively. It can be clearly depicted that our proposed framework improves the correlation with the ground truths.
\subsection{Machine Learning Classification of Cognitive Health}
We evaluate using machine learning classifiers to predict cognitive status of older adults using both individual modalities and combined features. We use leave-two-participants out method to train and test classification accuracy.

We first choose the individual activity features (machine learning method based interruption scores, sequencing scores, unsupervised scores) and their combined features to train and test cognitive impairment status classification for SMO based SVM algorithm \cite{cao06}. The classification accuracies are 72\%, 69\%, 76\% and 83\% respectively. Then we consider 7 EDA-activity features and 8 HRV-activity features individually in training and testing phase of SMO based SVM algorithm \cite{cao06} resulting 85\% and 80\% accuracy respectively.

\begin{figure}[!htb]
\begin{minipage}{0.24\textwidth}
\begin{center}
   \epsfig{file=combined_classification.pdf,height=1.2in, width=1.7in}
   \vspace{-.15in}
\caption{Individual and combined classification accuracies comparison with baseline method for cognitive impairment status detection}
   \label{fig:combined_classification}
\end{center}
\end{minipage}
\begin{minipage}{0.23\textwidth}
\begin{center}
   \epsfig{file=each_activity_cognitive_assessment.pdf,height=1.2in, width=1.7in}

\caption{Machine learning based cognitive health assessment accuracy for each complex activity in terms of activity, EDA and HRV features.}
   \label{fig:each_activity_cognitive_assessment}
\end{center}
\end{minipage}
\end{figure}

For combined classifier, we first applied sequential forward feature selection to find the best combinations of 1- 3 features for cognitive impairment classification group MCI, NCI and CI in terms of combined activity features (29 features), EDA-activity features (7 features) and HRV-activity features (8) features. Our final combined classifier (SMO based SVM algorithm \cite{cao06}) provides an accuracy of {\bf 93\%} in detecting the cognitive impairment status of older adults. Fig. \ref{fig:combined_classification} shows our proposed individual and combined methods outperform the baseline \cite{alam16} significantly (13\% improvement). Fig. \ref{fig:each_activity_cognitive_assessment} shows the cognitive impairment status prediction accuracy for each modality (activity feature, EDA and HRV) per individual complex activity.
\subsection{Discussion}
If we exclude the postural activities from automated activity performance scoring, we find reduced statistical correlation with original task completeness performance for \{NCI, MCI, CI\} participant group (INT 0.53*, SEQ 0.21' and unsupervised 0.49'). However, if we skip our proposed motion artifact removal stage, we find reduced statistical correlation with \{NCI, MCI\} and \{MCI, CI\} groups of participants (EDA and HRV correlations respectively \{0.51*, -0.51*\} and \{-0.53*,0.46\}). To test our proposed motion artifacts removal impact on EDA signals more rigorously, we choose 5 random participants, engage one expert motion artifact annotator to annotate motion artifacts segment on each participant's first 30 minutes of complex dataset using recorded video and apply both baseline and our methods to detect motion artifact segments. While baseline method achieves 75.5\% (FP rate 20.3\%) accuracy in detecting motion artifact segments, \emph{AutoCogniSys} outperforms achieving 89.9\% (FP rate 8.9\%) accuracy. In terms of experience, we have seen 100\% acceptance of wearing wrist-band,  71\% of acceptance for signing consent on using cameras and 0\% failure rate of collecting continuous data.
\section{Conclusion}
We propose, \emph{AutoCogniSys}, an IoT inspired design approach combining wearable and ambient sensors embedded smart home design, extensive signal processing, machine learning algorithms and statistical analytics to automate cognitive health assessment in terms of complex activity performances and physiological responses of daily events. Additionally, our postural activity detection approach in diverse population cum improved activity performance measurement and fundamental physiological sensor artifacts removal from physiological sensors help facilitate the automated cross-sectional cognitive health assessment of the older adults. Our efficient evaluation on each modality (physical, physiological and ambient) and each activity mode proves that any of the mode (say single activity and single sensor) also can provide significant improved cognitive health assessment measure.

Answer: stationary sensors


Input: What did the decision to base the water rates on usage reflect?

Context:
Time to clean house in Paso Robles Home
Front Page » Time to clean house in Paso Robles
September 5, 2010 Opinion By JIM REED
I’d like to give you an update on the issue of our civil servants cramming hundreds of millions of dollars in spending down our throats after the people of Paso Robles voted down the water rate increase last November. The rate increase is being hung up in the courts by the City Attorney. What was supposed to be a quick issue to get in front of a judge, has been drug out as long as possible by the City Attorney.
Even if the courts throw out the current rate increase, I expect that our civil servants will just change a couple of words in the rate increase notice and force the same old plan on us again.
There is a real problem with the people we have hired to work for us in Paso Robles. It seems that decisions are made based on some agenda, even if it is contrary to citizens’ wishes.
City Councilmen Ed Steinbeck, Nick Gilman and Mayor Duane Picanco, on August 19th, voted unanimously to hire the same law firm employed by the City of Bell. You may have heard the recent news story about the City of Bell’s corrupt city representatives.
This law firm allowed the elected officials and City employees to pillage the General Fund for their own benefit, contrary to the rights and interests of the citizens. We are already paying several City employees $12,000 per month with equally ridiculous benefits and pensions. What does this say about our elected representatives?
I believe most residents are like me. We elect people we believe have our best interest in mind. Over the last few years I have seen that nothing is farther from the truth. The people we have elected have lost track of the fact that “the City” exists to protect and deliver services to the citizens. To them it is some all-important ideal they strive to cultivate and improve according to their agenda. They have forgotten that they are elected to represent the citizens.
We have an election coming up in November. We have the opportunity to elect some responsible, principled people to represent us. If we elect more people from within this system, we will get more of the same type of government. We need to look at where the new candidates stand. Will they lawfully represent the citizens of the city? Or, are they happy with the way things are being run?
We have stood together in the past and have made real significant changes in important matters that are going to affect our lives for years to come. There are several thousand citizens that made their voice heard on the water issue, more than enough votes to make a change in our city government.
Please come out and vote for a democratic representative governing body for Paso Robles instead of the tyrannical leadership that exists now.
Jim Reed is a longtime resident of Paso Robles.
Subjects: Opinion Paso Robles Paso Robles City Council Vote	Related:
<- Previous Next ->	Endless Summer Nights at Edna Valley, event photos Trial postponed for Paso Robles woman accused of forgery The comments below represent the opinion of the writer and do not represent the views or policies of CalCoastNews.com. (moderator@calcoastnews.com Comment Guidelines )
2 whatisup says:	09/13/2010 at 9:27 pm
pasoobserver – Here is something to observe and get you going in the right direction:
California Government Code Section 65584
(a) (1) For the fourth and subsequent revisions of the
housing element pursuant to Section 65588, the department shall
determine the existing and projected need for housing for each region
pursuant to this article. For purposes of subdivision (a) of Section
65583, the share of a city or county of the regional housing need
shall include that share of the housing need of persons at all income
levels within the area significantly affected by the general plan of
(2) While it is the intent of the Legislature that cities,
counties, and cities and counties should undertake all necessary
actions to encourage, promote, and facilitate the development of
housing to accommodate the entire regional housing need, it is
recognized, however, that future housing production may not equal the
regional housing need established for planning purposes.
(b) The department, in consultation with each council of
governments, shall determine each region’s existing and projected
housing need pursuant to Section 65584.01 at least two years prior to
the scheduled revision required pursuant to Section 65588. The
appropriate council of governments, or for cities and counties
without a council of governments, the department, shall adopt a final
regional housing need plan that allocates a share of the regional
housing need to each city, county, or city and county at least one
year prior to the scheduled revision for the region required by
Section 65588. The allocation plan prepared by a council of
governments shall be prepared pursuant to Sections 65584.04 and
65584.05 with the advice of the department.
(c) Notwithstanding any other provision of law, the due dates for
the determinations of the department or for the council of
governments, respectively, regarding the regional housing need may be
extended by the department by not more than 60 days if the extension
will enable access to more recent critical population or housing
data from a pending or recent release of the United States Census
Bureau or the Department of Finance. If the due date for the
determination of the department or the council of governments is
extended for this reason, the department shall extend the
corresponding housing element revision deadline pursuant to Section
65588 by not more than 60 days.
(d) The regional housing needs allocation plan shall be consistent
with all of the following objectives:
(1) Increasing the housing supply and the mix of housing types,
tenure, and affordability in all cities and counties within the
region in an equitable manner, which shall result in each
jurisdiction receiving an allocation of units for low- and very low
(2) Promoting infill development and socioeconomic equity, the
protection of environmental and agricultural resources, and the
encouragement of efficient development patterns.
(3) Promoting an improved intraregional relationship between jobs
(4) Allocating a lower proportion of housing need to an income
category when a jurisdiction already has a disproportionately high
share of households in that income category, as compared to the
countywide distribution of households in that category from the most
recent decennial United States census.
(e) For purposes of this section, “household income levels” are as
determined by the department as of the most recent decennial census
pursuant to the following code sections:
(1) Very low incomes as defined by Section 50105 of the Health and
(2) Lower incomes, as defined by Section 50079.5 of the Health and
(3) Moderate incomes, as defined by Section 50093 of the Health
and Safety Code.
(4) Above moderate incomes are those exceeding the moderate-income
level of Section 50093 of the Health and Safety Code.
(f) Notwithstanding any other provision of law, determinations
made by the department, a council of governments, or a city or county
pursuant to this section or Section 65584.01, 65584.02, 65584.03,
65584.04, 65584.05, 65584.06, 65584.07, or 65584.08 are exempt from
the California Environmental Quality Act (Division 13 (commencing
with Section 21000) of the Public Resources Code).
pasoobserver says:	09/13/2010 at 6:52 pm
To whatisup —- First of all, I reviewed AB 602 Assembly Bill. Thanks. I am sorry to inform you but AB 602 is not the LAW as you so stated in your blog. I contacted the Deputy Chief Council’s office in Sacramento handling AB 602 to confirm your misstatement of facts. You know,in the English language, It shouldn’t be so difficult to answer some simple questions with a “YES” or “NO” answer. Yet, you are reluctant to do so, but you go on and on with a thesis along with some rhetoric. I never talked about a court suit over the “water issue”, I asked YOU, not about waiting for a court decision. Maybe, you did with some other people. Also, I was not ranting about the wineries usage of water. My response to you on your vague question about “there are people not paying their fair share for their use of water”. I related, are you talking about the wineries? I am well aware that most of the wineries are outside the city limits using the same aquifer. You took my question out of context., nice try! You are just being a popinjay and rhetorical. Also, you didn’t answer another question about “what is the unit cost of water” in Templeton? as compared to Paso Robles.
whatisup says:	09/13/2010 at 8:54 pm
I am on a well. I am sure you are capable of doing your own homework. I also am quite sure if you really contacted the Deputy Chief Counsel’s Office you have been set straight. What I gave you is a proposed small adjustment in the wide range of laws that make up the California Housing element. I assumed you could stumble onto the facts based on what I gave you. By the way, I believe you can review the Paso Robles Housing element plan on the City’s website or at the Library. The California Housing Element Laws that all cities and counties have to follow have been in place for almost 25 years. I realize you don’t actually have a clue how to look the laws up. Either educate yourself or keep making a fool of yourself, your choice. A simple Google search of California Housing Element Laws will get you going. Good Luck!
TO WHATISUP — I WOULD LIKE TO KNOW WHAT LAW YOU ARE REFERRING TO THAT SAYS “WE” THE PEOPLE HAVE TO SUBSIDIZE NEW DEVELOPMENT? AGAIN, FOR THE THIRD TIME, YOU FAILED TO ANSWER MY QUESTIONS POSED TO YOU IN MY PRIOR RESPONSES TO YOU ON SEPT.10TH &11TH. IS THERE A REASON WHY YOU DON’T WANT TO ANSWER THEM? YOU DO WHAT OUR ELECTED OFFICIALS DO SO WELL, AND THAT IS “IN ONE EAR AND OUT OF THE OTHER EAR” IT SEEMS TO ME THAT YOU ARE EITHER EMPLOYED BY THE CITY OR YOU HAVE OTHER DEALING WITH THE CITY, SO BE IT. IT APPEARS TO ME THAT YOU THINK THE CITY DOES EVERYTHING RIGHT. APPARENTLY, YOU PRESENT YOURSELF AS BEING VERY BIAS ON CITY DECISIONS. IT LIKE THEY CAN’T DO ANYTHING WRONG ACCORDING TO YOUR LOGIC. THEY KNOW WHAT IS BEST FOR THE CITIZENS OF PASO,THAT IS A GOOD EXAMPLE OF ARROGANCE ALONG WITH NARCISSISM.
WHAT PEOPLE ARE YOU TALKING ABOUT THAT DOESN’T PAY THEIR FAIR SHARE OF WATER? ARE YOU REFERRING TO THE WINERIES USING THE SAME AQUIFER?
I BELIEVE YOU RELATED THAT YOU RESIDE IN TEMPLETON, BUT YOU OWN PROPERTY IN PASO. BY THE WAY, WHAT IS THE COST PER UNIT OF WATER USAGE IN TEMPLETON COMPARED TO PASO? OF COURSE, TEMPLETON IS IN AN UNINCORPORATED AREA (COUNTY JURISDICTION).
WELL, I GAVE YOU SOME SUGGESTIONS ON HOW TO PAY FOR THE NACIMIENTO WATER PIPELINE AND SEWER TREATMENT PLANT. ALSO, REMEMBER IT’S THE CITIZENS’ MONEY THAT IS BEING SPENT. WHAT IS MOST IMPORTANT OF ALL, IS LET THE CITIZENS OF PASO DECIDE WITH THEIR VOTE ON HOW TO FINANCE THIS HUGE CAPITAL IMPROVEMENT PROJECT EXPENDITURE. JUST BE IN COMPLIANCE WITH STATE PROPOSITION 218 AND STOP CIRCUMVENTING THE LAW.
WOULD YOU OBJECT TO HAVING TO FINANCE SOME NEW BONDS ON YOUR PROPERTY TAX BILL AS A ” SPECIAL TAX” OR AN ASSESSMENT TAX” TO PAY FOR THE NACIMIENTO WATER PIPELINE AND SEWER TREATMENT PLANT? A PERCENTAGE OF PASO CITIZENS FINANCE LOCAL SCHOOL BONDS ON THEIR PROPERTY TAX BILL AND DON’T HAVE ANY KIDS GOING TO SCHOOL. HOW ABOUT THAT COMPARISON FOR YOU TO THINK ABOUT? WHAT SAY YOU?
I say less CapsLock, please.
whatisup says:	09/12/2010 at 11:41 pm
I have answered your questions. I have been quite detailed in my answers and I am sorry if you can’t deal with the detail. I guess it is your inconvenient truth. You do seem to like to deflect and go around in circles. Another example, now you are ranting about the wineries using the same aquaifier as the City. Let me be clear for you, I don’t like the amount of water the wineries are using. However, the wineries are in the County, not in the City and the City can’t do anything about it. They wineries are allowed to take the water they are taking even if it drops the City’s water levels in their wells. You need to complain to Sacramento. It sounds like you just don’t want to pay anything for the infrastructure because you really just don’t want it built.
Several of your observations of my opinions are bizarre considering I have stated several times I believe the Courts need to decide if Paso Robles has, or has not followed the rules as to funding the infrastucture. Obviously, as I have stated before, if the City loses the lawsuit the infrastructure will have to be paid out of the City’s General Fund until a new method of payment is voted on by the Citizens of Paso Robles. Pretty clear.
Your idea of charging based on a usage rate rather than a special assessment means that people who use little water, but live on a more expensive property might pay less overall, despite their property value. In addition, how do you deal with a rental unit where the renter is supposed to pay the water bill? Your idea is inherantly unfair, but my guess is it will favor you, so you don’t care if it is unfair and other people would pay part of your share. You also have decided that since I have alternative ideas to yours I must work for, or have business with the City of Paso Robles, another attempt to deflect from the issue. However, once again, I have never worked for the City or have ever done business with the City and don’t expect to ever do business with the City. I do own property in the City which is why I pay attention. Finally, it turns out there needs to be a fix to the housing element laws, the existance of which you are questioning. As I understand it the fix to the housing elemnt laws is because of some lawsuit. This should give you all the information you need to educate yourself on the California Housing Element laws that every city and county in California has to follow:
BILL ANALYSIS ————————————————————
|SENATE RULES COMMITTEE | AB 602|
|Office of Senate Floor Analyses | |
|1020 N Street, Suite 524 | |
|(916) 651-1520 Fax: (916) | |
|327-4478 | |
———————————————————— THIRD READING
Bill No: AB 602
Author: Feuer (D), et al
Amended: 8/20/10 in Senate
SENATE TRANSPORTATION & HOUSING COMM : 6-3, 6/29/10
AYES: Lowenthal, DeSaulnier, Kehoe, Pavley, Simitian, Wolk
NOES: Huff, Ashburn, Harman
ASSEMBLY FLOOR : Not relevant
SUBJECT : Statute of limitations on housing element
SOURCE : California Rural Legal Assistance Foundation
Housing California DIGEST : This bill states the intent of the Legislature
in enacting this bill to modify the courts opinion in Urban
Habitat Program v. City of Pleasanton (2008) 164
Cal.App.4th 1561, with respect to the interpretation of
Section 65009 of the Government Code, and revises and
clarifies statute of limitations and remedies for specified
housing related challenges.
Senate Floor Amendments of 8/20/10 revise the statute of
limitations and remedies for specified housing-related
ANALYSIS : The Planning and Zoning Law requires cities
and counties to prepare and adopt a general plan, including
a housing element, to guide the future growth of a
community. Following a staggered statutory schedule,
cities and counties located within the territory of a
metropolitan planning organization (MPO) must revise their
housing elements every eight years, and cities and counties
in rural non-MPO regions must revise their housing elements
every five years. These five- and eight-year periods are
known as the housing element planning period.
Before each revision, each community is assigned its fair
share of housing for each income category through the
regional housing needs assessment (RHNA) process. A
housing element must identify and analyze existing and
projected housing needs, identify adequate sites with
appropriate zoning to meet its share of the RHNA, and
ensure that regulatory systems provide opportunities for,
and do not unduly constrain, housing development. The
reviews both draft and adopted housing elements to
determine whether or not they are in substantial compliance
with the law. The Planning and Zoning Law and the Subdivision Map Act
also includes a number of sections governing zoning and
entitlements specifically related to housing, including:
? The Housing Accountability Act, which requires a city or
county to make one or more specified findings in order to
disapprove a particular housing development.
? A provision requiring cities and counties, when adopting
an ordinance which limits the number of housing units
which may be constructed on an annual basis, to make
findings as to the public health, safety, and welfare
benefits that justify reducing the housing opportunities
of the region. ? Density bonus law, which requires cities and counties to
grant a developer a density bonus, incentives, and
concessions when the developer proposes to include
specified percentages of affordable housing within a
development. ? The Least Cost Zoning Law, which requires cities and AB 602
counties to designate and zone sufficient vacant land for
residential use with appropriate standards to meet
housing needs for all income categories and to contribute
to producing housing at the lowest possible cost.
? A requirement that, when determining whether to approve a
tentative subdivision map, a city or county shall apply
only those ordinances, policies, and standards in effect
as of the date the developer’s application is deemed
Prior to a recent court decision, it was understood that
current law allowed a party to challenge the adequacy of a
city’s or county’s housing element at any time during a
planning period, provided that the challenger brought the
action “in support of or to encourage or facilitate the
development of housing that would increase the community’s
supply of [affordable] housing.” The challenging party was
required first to serve the city or county with a notice
identifying the deficiencies in the housing element. After
60 days or the date on which the city or county took final
action in response to the notice, whichever occurred first,
the challenging party had one year to file the action in
court. This process and statute of limitations also
applied to actions brought pursuant to the housing-related
statutes listed above. In 2006 Urban Habitat Program brought suit to challenge the
City of Pleasanton’s housing policies, including the city’s
annual cap on housing permits and the city’s cap on the
aggregate number of permissible housing units, both of
which Urban Habitat claimed were insufficient to allow the
city to meet its RHNA obligation. In 2008, the First
District California Court of Appeals issued an unpublished
decision in the case of Urban Habitat Program v. City of
Pleasanton allowing the case to proceed with respect to
some causes of action, but ruling that the challenge to the
housing element itself was time-barred. The court stated:
Although the statute does not specify the time within
which [a deficiency] notice must be given, it is our
conclusion that the statute must be interpreted as
containing a time limit within which this requirement
must be met? In sum, a party bringing a challenge AB 602
governed by section 65009, subdivision (d), has 90
days from the date a legislative action is taken or
approval is given to notify the local land use
authority of any claimed deficiencies in such an
action or approval. Its claim then accrues 60 days
after it gives this notice.
In other words, instead of being able to initiate a
challenge to a deficient housing element at any time during
the planning period, housing advocates and other interested
parties may now only initiate such a challenge by
submitting a deficiency notice within 90 days of the
housing element’s adoption.
1.Removes from the current list of city or county actions
which may be challenged pursuant to Government Code 65009
notice and accrual provisions those actions related to
the Housing Accountability Act, the Subdivision Map Act,
and the application of a Density Bonus ordinance to a
particular project, all of which are project-specific
actions. The bill maintains the ability to use these
notice and accrual provisions to challenge the adequacy
of a city’s or county’s density bonus ordinance
2.Extends lengthening the time in which a deficiency notice
may be served to cover all remaining city or county
actions described in this section of law, as opposed to
just housing element challenges. In other words, the
amendments apply the longer timeframe to serve the
deficiency notice to actions relating to the Least Cost
Zoning Law, annual limits on housing permits, and the
adequacy of a density bonus ordinance, in addition to
housing element law. 3.Provides that an entity challenging such an action in
support of affordable housing may serve the deficiency
notice up to five years after the city’s or county’s
action. After 60 days or the date on which the city or
county takes final action in response to the notice,
whichever occurs first, the challenging party has one
year to file an action in court, except that the lawsuit AB 602
may not be filed more than five years after the city’s or
county’s action. In other words, the entity must file
the lawsuit within one year of the expiration of the
deficiency notice or within five years of the city’s or
county’s action, whichever occurs first.
4.Provides that a housing element from a prior planning
period may not be challenged if the city or county has
adopted a revised housing element for the new planning
Government Code 65755 . Current law requires a court, if it
finds any portion of a general plan, including a housing
element, out of compliance with the law, to include within
its order or judgment one or more of the following remedies
for any or all types of developments or any or all
geographic segments of the city or county until the city or
county has complied with the law:
? Suspend the authority of the city or county to
issue building permits.
grant zoning changes and/or variances.
grant subdivision map approvals.
? Mandate the approval of building permits for
residential housing that meet specified criteria.
? Mandate the approval of final subdivision maps for
housing projects that meet specified criteria.
? Mandate the approval of tentative subdivision maps
for residential housing projects that meet specified
This bill clarifies that in any action or proceeding
brought pursuant to the notice and accrual provisions of
Government Code Section 65009 described above, neither the
court remedies described above nor any injunction against
the development of a housing project shall abrogate,
impair, or otherwise interfere with the full exercise of
the rights and protections granted to an applicant for a
tentative map or a vesting tentative map under specified
provisions of the Subdivision Map Act or to a developer
under a specified provision relating to development AB 602
Under current law, HCD operates a number of grant programs
to which cities and counties may apply. In many cases, the
law requires a city or county to have an HCD-approved
housing element in order to be eligible for funding. This bill provides that if a third-party challenges the
adequacy of a housing element in court and the court finds
that the housing element substantially complies with all of
the requirements of housing element law, the element shall
be deemed to be in compliance for purposes of state housing
The statutory language interpreted by the court and at
issue in this bill was added to statute by AB 998 (Waters),
Chapter 1138, Statutes of 1983, a bill sponsored by the
League of California Cities and the California Building
Industry Association. AB 998 created a short statute of
limitations period for land use decisions generally but
provided a specific exception to protect the ability to
challenge deficient housing elements. The Senate Housing
and Land Use Committee and the Senate Third Reading
analysis of the bill stated that the bill:
Specifies that for challenges in support of low- and
moderate-income housing requirements, the petitioner
shall notice local government 60 days prior to filing
action. The [one-year] statute of limitations then
begins on the first day the legislative body fails to
In the intervening 25 years prior to the Urban Habitat
ruling, housing advocates filed and successfully settled at
least ten cases in which the 60-day deficiency notice was
sent more than 90 days after adoption of the city’s or
county’s housing element. In none of these cases was the
timeliness on the advocates’ suit contested. Likewise, six
bills amended other portions of this statute during those
intervening years, and there was never any controversy
surrounding the lack of a deadline for housing advocates to
serve a deficiency notice nor any attempt to change the AB 602
statute in this regard. Current level of housing element compliance . According to
HCD’s website as of June 7, 2010, only 46 percent of cities
and counties have adopted an HCD-approved housing element
for the current planning period that began in 2005 for the
San Diego region, 2008 for the Southern California, Fresno,
Kern, and Sacramento regions, and the summer of 2009 for
the remaining areas of the state. Unlocking the private market . The purpose of housing
element law is to create opportunities for the private
housing market to function. Builders cannot build without
access to appropriately zoned land, and current land use
plans in many cities and counties in California fail to
provide sufficient opportunities to accommodate projected
population growth. The San Diego Association of
Governments’ Regional Comprehensive Plan describes this
typical California paradox in the following way:
Under current plans and policies, more than 90 percent
of [the San Diego region’s] remaining vacant land
designated for housing is planned for densities of
less than one home per acre, and most is in the rural
back country areas dependent upon scarce groundwater
supplies. And of the remaining vacant land planned for
housing in the 18 incorporated cities, only about
seven percent is planned for multifamily housing. When
taken together, the current land use plans of the 19
local jurisdictions do not accommodate the amount of
growth anticipated in our region. SANDAG’s population
forecast, which reflects the current adopted local
land use plans in the region, projects that while
population will increase by 37 percent by 2030,
housing will grow by just 30 percent. The forecast
shows that if local plans are not changed, demand for
housing will continue to outpace the supply, just as
Housing element law addresses this problem directly by
requiring cities and counties to zone land at appropriate
densities to accommodate the projected housing needs of all
income groups and to remove constraints that prevent such
sites from being developed at the allowed densities. AB 602
Cities and counties, however, are not required to build
housing because that is the role of private developers.
The law holds cities and counties accountable only for that
which they control: zoning and land use entitlements.
Without the ability to enforce housing element law, the
market’s ability to meet housing demand may well remain
locked up.
FISCAL EFFECT : Appropriation: No Fiscal Com.: No
SUPPORT : (Verified 8/23/10)
California Rural Legal Assistance Foundation (co-source)
Housing California (co-source)
Advocates for Affordable Homes in Fremont
California Coalition for Rural Housing
Community Housing Improvement Program
Community Housing Works
Eden Housing
Fair Housing of Marin
Grassroots Leadership Network of Marin
Kennedy Commission
Public Advocates, Inc
San Diego Housing Federation
Self-Help Enterprises
Sierra Club of California
American Planning Association, California Chapter
JA:nl 8/23/10 Senate Floor Analyses SUPPORT/OPPOSITION: SEE ABOVE
pasoobserver says:	09/11/2010 at 11:17 pm
To whatisup — Thank you for your response to my comments. However, you failed to answer some of my questions that I mentioned to you. It’s almost like dealing with some City officials. They just let the public vent at their bimonthly council meetings. In my opinion, it’s difficult to deal with narcissism and arrogance. Over the years, there has been some very good input to our elected officials on how to proceed on the Nacimiento water pipeline,but it fell on deaf ears. You wanted me to answer some of your questions,but you did not answer some of my questions. Again, are you willing to subsidize new development?,Yes?or No?, are you willing to pay for a commodity that you are not receiving? Yes?or No? and another question for you. Are you willing to pay over 300% on your water bills within the five (5) year plan that the City has proposed? Also, the water rates will be subject to later increases too. By the way, I do concur with the city’s plan of “you pay for the amount of water units you use”. (748 gal=one unit). However, the higher water rates are not good for our senior citizens on fixed incomes and other struggling families in our community. My first suggestion years ago was desalination. The response was it was too expensive. Of course, now it is more expensive. I would suggest that our elected officials recall the existing bonds (The bonds can be recalled early). The City council can explain to the citizens in detail with financing of new bonds at a lower interest rate as of now for the sewer plant and Nacimiento water pipeline and present their new proposal in compliance with Proposition 218. Let the citizens of Paso VOTE on the financing bonds for their approval. Most of the citizens,that I had spoken to were not happy with the way our City Council handled the Nacimiento water pipeline project. The citizens of Paso didn’t give our City Council a “BLANK CHECK” for $176 million to spend without voter approval. I would suggest that it be a “special tax” or “an assessment” be levied on our property taxes. A percentage of those bonds can be deducted on Federal Income taxes. As it is now, a” fee” on a capital funding project is not deductible. Of course, there are homeowners would not go for this suggestion due to our poor economy. My analogy mentioned above would be, you would get something back on a “special tax” or an “assessment” verses nothing on a “fee”. What say you?
whatisup says:	09/12/2010 at 9:02 am
Unfortunately the law says we have to subsidize new development in California. I don’t like it, but it is the law. I know paying using the property taxes was bandied about. The argument against it was it would mean some would be paying less than their potential usage and others could be light water users, but pay a significant amount if usage rates were higher. I think the decision that was made to base it on usage was out of a desire to maximize revenue. It seems to me if people are using water and not paying their share of the costs it is not fair. The Senior issue is very difficult. If someone is retired for twenty years is it realistic to think prices don’t go up during the 20 years of retirement. Think what prices were in 1990 compared to today. Should Seniors never have to pay for capital improvements? Paso Robles also had very low water rates. Rates that are no longer possible given the circumstances. Desalination will happen eventually. California is out of water. If you want to pay $1,000,000 a gallon there is no more allotable water of any consequence in California. The expense will be tremendous — still have to build a desalination plant, still have to build a pipeline. I don’t know if the plant has to be built along the ocean or if the salt water could be piped over to Paso Robles. If it has to be built along the ocean, Paso Robles doesn’t own land on the ocean and, in any case, the environmentalists will keep it in courts for years as they have done so for other proposed desalination plants in Southern California. Eventually necessity will force desalination past the environmentalists, but not yet.
pasojim says:	09/13/2010 at 7:46 am
Whatisup – On one of your previous post you made the comment you haven’t heard any of the legal suggestions for the water issue, But you obviously have. That is a good thing. So we can move the discussion ahead.
Once, again this was handled incorrectly by our city custodians from the beginning. And now here we are. The public is not supporting this very expensive, very limited benefit project. As you said, until a plan is developed that the public can support, things don’t look good.
All this discussion about the water issue has only reinforced my opinion the issue hasn’t been about water, only how the plan should be paid for. Or more specifically, to what extent do we allow our elected custodians and our un-elected GOD tzar decide which laws they will follow and which laws they will ignore. When the City GOD tzar tell citizens at a council meeting if we don’t agree with the City’s plan, then we should just sue him, and when the City Attorney explains to a citizen at a City Council meeting that she does have to respond to their questions because she does NOT work for them. When the project is voted down by the citizens and the council brings it right back up, it is clear that our elected representatives are not doing their job providing direction to their employees and listening to and representing the CITIZENS.
The subject of the original post was the need to elect different representation. I think with all the conversation made on this post, as well as the post on Cal Coast about the hiring of the new legal firm you were involved in, Supports my original opinion.

Answer: Revenue maximization.