Training and evaluation record

v1 provenance

Artifact type: trained XGBoost classifier on engineered pose features — input is a 56-column pandas DataFrame, not images and not a CSV file at inference
Model: xgboost.XGBClassifier trained from scratch (no YOLO fine-tuning; YOLO26x-Pose used only upstream to generate the feature CSV for training)
Upstream pose detector (data generation only, out of scope for classifier): Ultralytics YOLO-Pose (yolo26x-pose.pt), pretrained, batch_size=32, det conf 0.20, kp conf 0.25, PyTorch AMP
CTSPL training run: Fall Detection v1 (Experiment 01)
CTSPL fine-tuning run: none
Training dataset: pose_benchmark_priority1_dataset.csv — 6,607 samples after 1:1 class balancing (58 columns total = 51 pose features + 5 additional + 1 split + 1 label = 58; XGBoost uses only 51 + 5 = 56 classifier features, columns 1–56)
Raw image source for CSV generation (upstream): /home/ctspl/model_training/fall/version3/data (train/val/test with fall/normal subdirs)
Training hardware: CPU-only XGBoost (no GPU required for classifier); upstream YOLO extraction used CUDA
Training date: 2026-09-21
Selected checkpoint: models/xgboost_priority1_fall_model.pkl (input 56 features → output Fall/Normal)

TRAINING:  CCTV IMAGE → YOLO26x-Pose → 51 features → +5 → CSV (58 cols) → DataFrame → XGBoost
INFERENCE: upstream DataFrame (51 + bbox = 55 cols) → run.py: +5 → 56-col DataFrame → XGBoost (no CSV into 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.

Upstream (data generation only, out of scope for classifier inference):

Extract 17 keypoints per detected person using YOLO-Pose (each with x, y, confidence → 51 features)
Training CSV has 58 columns total — 51 pose features + 5 additional Priority 1 features + 1 split + 1 label = 58 columns

At inference, upstream delivers a pandas DataFrame with the 51 keypoint columns + bounding box; scripts/run.py computes the 5 Priority 1 features as columns and passes the 56-column DataFrame to XGBoost.

In pose_extract.py: row = norm_kpts + [aspect_ratio] + p1_features + [split, label] where norm_kpts = 51 and [aspect_ratio] + p1_features = 5.

Very important: XGBoost classifier does not use split or label as input. It uses only 51 + 5 = 56 classifier features (columns 1–56). Columns 57 → split (train/val/test) and 58 → label (0 = Normal, 1 = Fall) are metadata, not model input.

The 2 metadata columns are:

split: which dataset split the row came from (train, val, test)
label: ground-truth class (0 = Normal, 1 = Fall)

Training configuration

Upstream pose extraction (data generation only, not classifier training):

Model: yolo26x-pose.pt (Ultralytics)
Batch size: 32
Detection confidence threshold: 0.20
Keypoint confidence threshold: 0.25 (for CSV generation; inference threshold for NaN masking is 0.50)
Compute: PyTorch AMP mixed precision (CUDA) — only for extraction

Feature engineering (output is classifier input — 51 + 5 = 56, with 58 columns total in CSV):

51 pose features (cols 1–51): normalized keypoint features (17 COCO joints × 3: x_norm, y_norm, confidence) where x_norm=(x-x1)/w, y_norm=(y-y1)/h
- 5 additional Priority 1 features (cols 52–56):
- aspect_ratio = w / h (col 52)
- nose_relative_y = (Y_nose - y1) / h (col 53)
- torso_angle = angle HipMid→ShoulderMid vs. Y-axis (col 54)
- norm_com_y = confidence-weighted center of mass Y (col 55)
- head_hip_v_dist = (Y_hip_mid - Y_nose) / h (col 56)
= 56 classifier features (XGBoost input, columns 1–56)
- 1 split (col 57, train/val/test) + 1 label (col 58, 0/1) = 58 columns total in CSV

In pose_extract.py: row = norm_kpts + [aspect_ratio] + p1_features + [split, label] → 51 + 5 + 1 + 1 = 58. XGBoost uses only columns 1–56.

XGBoost hyperparameters:

n_estimators: 150
max_depth: 5
learning_rate: 0.03
scale_pos_weight: 1.0 (data pre-balanced)
missing: np.nan
eval_metric: logloss
random_state: 42

Classifier I/O:

Input: (n, 56) float pandas DataFrame (one row per person); CSV never enters the model at inference
Output: Fall / Normal via predict / predict_proba; rule P(Fall) >= 0.70 → Fall

Training performance record

Standard test set evaluation (56-feature test vectors):

Training completed successfully. Model saved to models/xgboost_priority1_fall_model.pkl.

--- XGBoost Classification Report (56 features → Fall/Normal) ---
              precision    recall  f1-score   support

  Normal (0)       0.90      0.91      0.91      3207
    Fall (1)       0.92      0.91      0.91      3400

    accuracy                           0.91      6607
   macro avg       0.91      0.91      0.91      6607
weighted avg       0.91      0.91      0.91      6607

Overall accuracy: 91.00%

Grid search (classifier thresholds only: CONF_THRESH 0.25/0.50, FALL_PROB_THRESH 0.20–0.95) achieved best: Accuracy 92.67%, F1 90.91% at CONF=0.50, Fall_Prob=0.70. See README.md / docs/spec.md for full table.