Data record

Model training data

The XGBoost classifier is trained on the engineered feature dataset derived upstream from a merged image collection. The image collection itself is upstream and out of scope for the classifier — at training the classifier sees the CSV loaded as a 56-column DataFrame; at inference it sees a 56-column DataFrame built by scripts/run.py from an upstream 51+bbox DataFrame.

Upstream image collection (used only to generate the feature CSV)

Data collector / dataset owner: Curated from public Roboflow Universe datasets (Hong Nguyễn, Ludovico Valenti, Final Year Project, and IIUI Impact).
Dataset version or snapshot: Combined Snapshot v1.
Annotation format (upstream): YOLO / Roboflow Object Detection format.
Curation notes: Source datasets were merged, deduplicated, and balanced to eliminate class bias.
Upstream links / references:

Dataset counts and split summary (upstream image counts)

Overall sample count & class distribution

Class	Sample count	Percentage
Fall (`fall` / `fall-detected` / `sitting` )	23,300	50.0%
Normal (`normal` / `standing` / `walking`)	23,300	50.0%
Total	46,600	100.0%

Train / Validation / Test split breakdown

Split	Fall samples	Normal samples	Total split count	Percentage
Train	16,310	16,310	32,620	70.0%
Validation	4,660	4,660	9,320	20.0%
Test	2,330	2,330	4,660	10.0%
Total	23,300	23,300	46,600	100.0%

Benchmark & evaluation sources (upstream)

Source Dataset	Upstream Version	Local File Format	Primary Classes Tracked
Hong Nguyễn	v4	JPG / TXT (YOLO)	Fall, Non-Fall
Ludovico Valenti	v2	JPG / TXT (YOLO)	Fall, Stand
Final Year Project	v2	JPG / TXT (YOLO)	Human Fall
IIUI Impact	v5	JPG / TXT (YOLO)	Fall-Detected, Sitting, Walking, Standing

Independent validation data

Hold-off validation datasets reserved for independent testing must remain outside the repository commit tree. The evaluator must record the source window, camera angles, sampling rate, and license permissions prior to benchmark execution.

Data Record: Extracted Pose Benchmark Dataset (Classifier Training Data)

This document records the specifications for the CSV dataset that directly trains the XGBoost classifier (pose_benchmark_priority1_dataset.csv). At training time the CSV is loaded into a pandas DataFrame; at inference time upstream delivers a DataFrame of 51 features + bbox and scripts/run.py computes the 5 features in memory (no CSV into the model).

Inference input contract (what goes IN to run.py):

Columns	Count	Source
`x_i, y_i, conf_i` for i = 0…16	51	Upstream (input contract)
`x1, y1, x2, y2`	4	Upstream bbox (needed to compute the 5)
	55	Total input to `run.py`

The extra 5 features are extracted in run.py, not received. Model input after extraction = 56 = 51 + 5.

TRAINING:  CCTV IMAGE → YOLO26x-Pose → 51 features → +5 → CSV (56 features + split + label) → DataFrame → XGBoost
INFERENCE: upstream DataFrame (51 + bbox, 55 cols) → run.py: +5 → 56-col DataFrame → XGBoost   (no CSV into model)

1. Primary Dataset Summary

Dataset Name / Export Path: pose_benchmark_priority1_dataset.csv
Upstream Image Directory (for generation only): /home/ctspl/model_training/fall/version2/fall_no_fall_data
Extractor Architecture (upstream, not part of classifier): yolo26x-pose.pt (Ultralytics YOLO Pose Estimation)
Confidence Cutoff (CONF_THRESH for NaN masking): 0.25 (training generation) / 0.50 (inference threshold)
Label Mapping:
- 0: Normal / Non-Fall
- 1: Fall
Direct XGBoost Input: 56-column pandas DataFrame (one row per person) = 51 normalized keypoint features (17 × 3) + 5 Priority 1 features. No image data; no CSV into the model at inference.
Feature Scope: 17 COCO keypoints normalized to bounding box + 5 Priority 1 CCTV-invariant features. Each row = one person.

The system will (upstream of XGBoost): 1) Extract 17 keypoints per person using YOLO-Pose (51 features), 2) Calculate 56 features total per person (51 normalized + 5 Priority 1) — the 56-vector is the direct XGBoost input. Older docs described this as “58 features (52 + 5)”; correct count is 56 = 51 (17×3) + 5. Total CSV columns = 58 (56 features + split + label).

2. Feature Schema & Column Definitions

The generated CSV contains **56 feature columns **.

A. Normalized Keypoint Features (Columns 1 – 51)

17 COCO keypoints × 3 values (x_norm, y_norm, confidence) where x_norm = (x - x1) / w, y_norm = (y - y1) / h relative to the person's bounding box. Low-confidence keypoints have x_norm/y_norm = NaN (confidence preserved). Order matches COCO indexing (0=nose … 16=right ankle). Header: x_0,y_0,conf_0,...,x_16,y_16,conf_16.

B. Priority 1 CCTV-Invariant Features (Columns 52 – 56) — total 5

aspect_ratio (col 52): Bounding box proportion w / h. High values indicate horizontal body orientation (falling/lying).
nose_relative_y (col 53): Normalized vertical height of the nose: $$\text{nose_relative_y} = \frac{Y_{\text{nose}} - Y_1}{h}$$
torso_angle (col 54): Angle of the torso vector (Hip Midpoint → Shoulder Midpoint) relative to vertical Y-axis: $$\theta = \text{atan2}(|\Delta X|, |\Delta Y|) \times \frac{180}{\pi}$$
norm_com_y (col 55): Vertical Center of Mass normalized across visible keypoints weighted by confidence: $$\text{norm_com_y} = \frac{\sum (Y_i \times c_i) / \sum c_i - Y_1}{h}$$
head_hip_v_dist (col 56): Vertical normalized distance between hips and head/nose: $$\text{head_hip_v_dist} = \frac{Y_{\text{hip_mid}} - Y_{\text{nose}}}{h}$$

C. Dataset Metadata & Supervision (Columns 57 – 58) — not model input

split: Source split identifier (train, val, or test).
label: Binary target label (0 for Normal, 1 for Fall).

For training/evaluation, the classifier uses only columns 1–56 as X (a 56-column DataFrame) and label as y (see scripts/train.py:70). At inference, upstream provides the 51 keypoint columns + bbox as a DataFrame; scripts/run.py computes columns 52–56 and passes the resulting 56-column DataFrame to predict_proba.