| { |
| "schema_version": 1, |
| "project": { |
| "name": "fall-detection", |
| "version": "1.0.0", |
| "task": "XGBoost fall classification from 56-feature pose vectors (image → YOLO upstream)", |
| "python": ">=3.10" |
| }, |
| "paths": { |
| "pose_model": "../yolo26x-pose.pt", |
| "classifier_model": "models/xgboost_priority1_fall_model.pkl", |
| "output_dir": "output" |
| }, |
| "models": { |
| "pose_model_type": "Ultralytics YOLO-Pose (upstream feature generation only, not classifier input)", |
| "classifier_type": "XGBClassifier", |
| "class_names": { |
| "0": "Normal", |
| "1": "Fall" |
| }, |
| "feature_count": 56, |
| "classifier_feature_count": 56, |
| "csv_total_columns": 58, |
| "column_breakdown": "51 pose features + 5 additional Priority 1 features + 1 split + 1 label = 58 columns; XGBoost uses only 51 + 5 = 56 (columns 1-56)", |
| "row_construction": "row = norm_kpts (51) + [aspect_ratio] + p1_features (4) + [split, label] → 51 + 5 + 1 + 1 = 58" |
| }, |
| "thresholds": { |
| "yolo_confidence": 0.25, |
| "keypoint_confidence": 0.5, |
| "fall_probability": 0.7, |
| "_notes": "yolo_confidence is upstream YOLO detection threshold (not a classifier parameter); keypoint_confidence < threshold → NaN x/y in 56-vector (preserved confidence); fall_probability is XGBoost P(Fall) decision threshold" |
| }, |
| "feature_schema": { |
| "keypoint_count": 17, |
| "values_per_keypoint": [ |
| "x_normalized", |
| "y_normalized", |
| "confidence" |
| ], |
| "keypoint_feature_count": 51, |
| "bounding_box_feature_count": 1, |
| "priority1_feature_count": 4, |
| "additional_feature_count": 5, |
| "additional_features": [ |
| "aspect_ratio", |
| "nose_relative_y", |
| "torso_angle", |
| "norm_com_y", |
| "head_hip_v_dist" |
| ], |
| "classifier_feature_count": 56, |
| "classifier_columns": "1-56", |
| "metadata_columns": [ |
| "split", |
| "label" |
| ], |
| "metadata_count": 2, |
| "metadata_indices": { |
| "split": 57, |
| "label": 58 |
| }, |
| "csv_total_columns": 58, |
| "csv_total_breakdown": "51 pose (cols 1-51) + 5 additional (cols 52-56) = 56 classifier features; + 1 split (col 57, train/val/test) + 1 label (col 58, 0=Normal/1=Fall) = 58", |
| "row_construction": "pose_extract.py: row = norm_kpts (51) + [aspect_ratio] + p1_features (4) + [split, label] → 51 + 5 + 1 + 1 = 58; XGBoost uses only 51 + 5 = 56 (columns 1-56)", |
| "column_indices": { |
| "pose_features": "1-51", |
| "additional_features": "52-56", |
| "split": 57, |
| "label": 58 |
| }, |
| "total_feature_count": 56, |
| "low_confidence_coordinates": "NaN", |
| "low_confidence_confidence_value": "preserved", |
| "bounding_box_features": [ |
| "aspect_ratio" |
| ], |
| "priority1_features": [ |
| "nose_relative_y", |
| "torso_angle", |
| "norm_com_y", |
| "head_hip_v_dist" |
| ] |
| }, |
| "input": { |
| "pipeline_csv_input": { |
| "type": "pandas.DataFrame", |
| "consumer": "scripts/run.py", |
| "description": "Upstream delivers a DataFrame with 51 normalized keypoint features + bounding box (55 columns total) per row; run.py extracts the 5 Priority 1 features (NOT input), builds a 56-column DataFrame (51+5), and passes that DataFrame (not a CSV) to XGBoost", |
| "keypoint_columns": "x_0,y_0,conf_0,...,x_16,y_16,conf_16 (51 columns, x/y normalized to bbox; NaN allowed for occluded)", |
| "keypoint_feature_count": 51, |
| "bbox_columns": ["x1", "y1", "x2", "y2"], |
| "bbox_feature_count": 4, |
| "input_column_count": 55, |
| "input_breakdown": "51 keypoint (x_i,y_i,conf_i i=0..16) + 4 bbox (x1,y1,x2,y2) = 55 total input to run.py", |
| "bbox_units": "pixels", |
| "computed_in_run_py": ["aspect_ratio", "nose_relative_y", "torso_angle", "norm_com_y", "head_hip_v_dist"], |
| "extracted_count": 5, |
| "model_input": "pandas.DataFrame, n rows × 56 columns (FEATURE_COLS = 51 input + 5 extracted)", |
| "model_feature_count": 56, |
| "output_columns": ["prediction", "fall_probability"], |
| "min_valid_keypoints": 5, |
| "cli_note": "CSV path on CLI is only upstream transport; it is loaded into a DataFrame before feature extraction" |
| }, |
| "classifier_input": { |
| "type": "feature_vector", |
| "dimensions": 56, |
| "dtype": "float", |
| "missing_value": "NaN", |
| "description": "Direct XGBoost input: 56-column pandas DataFrame (one row per person) = 51 pose features (17 keypoints × x_norm, y_norm, confidence) + 5 additional Priority 1 features; no image or CSV input at inference", |
| "column_breakdown": "51 + 5 = 56 classifier features (cols 1-56); cols 57-58 (split, label) are metadata, not input", |
| "very_important": "XGBoost does NOT use split or label as input features; label is supervision only" |
| }, |
| "upstream_image_input": { |
| "_note": "Images are processed upstream by YOLO26x-Pose to generate the 56-vector; not direct classifier input", |
| "formats": [ |
| ".jpg", |
| ".jpeg", |
| ".png", |
| ".bmp" |
| ], |
| "color_format": "BGR", |
| "default_image": null |
| } |
| }, |
| "output": { |
| "classifier_output": { |
| "type": "binary_label", |
| "labels": ["Normal", "Fall"], |
| "label_ids": {"Normal": 0, "Fall": 1}, |
| "decision_rule": "P(Fall) >= fall_probability (0.7) → Fall, else Normal", |
| "probabilities": ["P(Normal)", "P(Fall)"] |
| }, |
| "save_annotated_default": false, |
| "filename_prefix": "annotated_" |
| } |
| } |
|
|
