A15_Data/DataReadMe.md

A15 — Filter ugly exercises, rescore good ones & cut exercise segments

Overview

This directory takes the ~180 2D exercise videos, filters out exercises with poor form (predicted "ugly" by a CNN), rescales the remaining good exercises onto a normalised 0–4 quality score, cuts each clip to only the frames between the predicted start and stop of the exercise movement using the A12 start/stop detection model, and finally augments the cut sequences to expand the training dataset for downstream models.

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Pipeline

scores.csv (180 clips, raw quality scores)
    │
    ▼ filter_ugly_exercises.py
    │   • loads each clip from Datasets_all/kinect_good_preprocessed/
    │   • runs A_CNN.keras (sigmoid output: P(good))
    │   • threshold: ≥ 0.5 → GOOD, < 0.5 → UGLY
    │
    ├─ a15_predictions.csv   (179 clips processed, 1 missing Kinect file)
    ├─ a15_good_list.csv     (171 clips predicted GOOD)
    ├─ a15_ugly_list.csv     (  8 clips predicted UGLY)
    └─ a15_summary.txt       (text summary of the filter run)
                              │
                              ▼ rescore_good.py
                                  • min-max normalisation to 0–4
                                  • excludes A1_kinect (score=0.0) from
                                    the [min, max] range calculation
                                  • clips result to [0, 4]
                                  │
                                  └─ a15_good_rescaled.csv  (171 clips)
                                    │
                                    ▼ cut_with_a12.py
                                        • loads each clip from kinect_good_preprocessed/
                                        • engineers same 70 features as A12 training
                                          (distances, velocities, accelerations)
                                        • runs A_Kinect_Dense_relu_adam_bs64
                                          frame-by-frame → binary prediction
                                        • first 0→1 transition → START,
                                          last 1→0 transition → STOP
                                        • deletes sequences shorter than 30 frames
                                        │
                                        ├─ a15_cut/              (148 cut CSVs)
                                        └─ a15_cut_summary.csv   (per-clip metadata)
                                          │
                                          ▼ augment_cut_data.py
                                              • loads each cut CSV from a15_cut/
                                              • applies 4 spatial augmentations:
                                                — mirror on y-axis
                                                — rotate ±10° on y-axis
                                                — stretch/compress x,y,z
                                              • each variant inherits the original score
                                              │
                                              ├─ a15_cut_augmented/   (740 CSVs)
                                              └─ a15_augmented_data.csv   (740 rows)

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Files

File	Rows	Description
scores.csv	180	Original raw scores from upstream (Var1=clip, Var2=score)
a15_predictions.csv	179	Per-clip original scores + model good_probability + predicted_label
a15_good_list.csv	171	Subset predicted GOOD
a15_ugly_list.csv	8	Subset predicted UGLY
a15_good_rescaled.csv	171	Good clips with min-max normalised score (0–4)
a15_summary.txt	—	Human-readable summary of the filter step
a15_cut_summary.csv	171	Per-clip cutting metadata (status, start/stop frames, lengths)

Directories

Directory	Contents
a15_cut/	148 cut CSV files (≥ 30 frames each), frames trimmed to predicted exercise segment
a15_cut_augmented/	740 CSV files (148 original + 592 augmented), each with full frame-by-frame skeleton data

Utility scripts

Script	Purpose
filter_ugly_exercises.py	Connects scores.csv with Kinect CSVs, runs A_CNN inference, splits good/ugly
rescore_good.py	Min-max normalises good-clip scores to [0, 4]
rescore.py	(Original reference) — simple score / max * 4 rescale on all clips
cut_with_a12.py	Cuts each good clip to the exercise segment using the A12 start/stop classifier
augment_cut_data.py	Augments cut sequences via mirror, rotation, and stretch/compress transformations

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Rescaling method (rescore_good.py)

The original rescore.py uses a simple linear rescale:

score_rescaled = (score / max_score) * 4

This clustered most good clips between 3 and 4, providing poor discrimination. The updated rescore_good.py uses min-max normalisation, which spreads scores evenly across the full range:

score_rescaled = ((score - min_score) / (max_score - min_score)) * 4
               → clamped to [0, 4]

Calibration set: 170 good clips (all except A1_kinect, which has score=0.0 — an artifact, not a meaningful observation).

Statistic	Value
Min (maps to 0)	0.8879 (A24_kinect)
Max (maps to 4)	1.3354 (B2_kinect)
Range width	0.4475

A1_kinect is kept in the output with its clipped score of 0.0, but excluded from the min/max calculation so it does not compress the range for all other clips.

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Cutting method (cut_with_a12.py)

After filtering and rescoring, each clip undergoes frame-level start/stop detection using the best-performing model from A12: A_Kinect_Dense_relu_adam_bs64 (Dense architecture, 70 input features, 3-fold CV F1 = 0.949).

Steps

1. Feature engineering — joint distances (hand-to-shoulder, head-to-hip, etc.), velocities, and accelerations are computed for every frame, matching the exact feature set used in A12 training.
2. Frame classification — the Dense model predicts exercise (1) vs non-exercise (0) for each frame independently.
3. Segment detection — the first 0→1 transition marks the start frame, and the last 1→0 transition marks the stop frame.
4. Cut — frames outside [start, stop] are discarded.
5. Length filter — sequences shorter than 30 frames (≈ 1 s at 30 fps) are considered too short and are removed.

Results

Metric	Count
Good clips processed	171
Cut OK (≥ 30 frames)	148
Too short, removed (< 30 frames)	23
Avg. cut length	129 frames (4.3 s)

Output columns (a15_cut_summary.csv)

Column	Description
clip	Exercise clip identifier
status	ok, too_short_N_frames, or missing_kinect_csv
n_frames_input	Number of frames in the original (uncut) CSV
n_frames_cut	Number of frames after cutting
start_frame	Predicted start frame (FrameNo value)
stop_frame	Predicted stop frame (FrameNo value)
cut_path	Path to the cut CSV file (empty if too short)

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Augmentation method (augment_cut_data.py)

After cutting, each sequence is augmented in-place to produce 4 additional variants per original clip, for a 5× expansion of the dataset (148 → 740). Only spatial augmentations that preserve the exercise label and quality score are applied.

Augmentations

Augmentation	Suffix	Description
Mirror on y-axis	_mirror	Negate all x-coordinates, mirroring left ↔ right
Rotate +10° on y-axis	_rotate_pos	3D rotation matrix around y-axis (+10°)
Rotate −10° on y-axis	_rotate_neg	3D rotation matrix around y-axis (−10°)
Stretch/compress	_stretch	Scale x by +5%, y by −5%, z by +2%

Each augmented variant keeps its original score_rescaled and good_probability — the augmentation changes only the skeleton coordinates, not the underlying quality of the exercise.

Input / output

Item	Path
Input directory	a15_cut/ — 148 frame-by-frame skeleton CSVs
Output directory	a15_cut_augmented/ — 740 CSVs
Metadata	a15_augmented_data.csv — clip, score_rescaled, good_probability

Output columns (a15_augmented_data.csv)

Column	Description
clip	Exercise clip identifier (e.g. A100_kinect_mirror)
score_rescaled	Min-max normalised quality score in [0, 4] (same as original)
good_probability	CNN sigmoid output — model confidence that the exercise is GOOD (same as original)

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Final output columns (a15_good_rescaled.csv)

Column	Description
clip	Exercise clip identifier (e.g. A100_kinect)
score_rescaled	Min-max normalised quality score in [0, 4]
good_probability	CNN sigmoid output — model confidence that the exercise is GOOD

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Supplementary output (a15_augmented_data.csv)

See the Augmentation method section above. This file combines original and augmented clips (740 rows) and is the primary training input for downstream models that benefit from a larger, spatially varied dataset.