Upload fall detection code, trained models, and repeated experiments

.gitignore

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+__pycache__/
+*.py[cod]
+.pytest_cache/
+.venv/
+data/videos/
+data/processed/
+artifacts/models/
+artifacts/experiments/*/
+!artifacts/experiments/.gitkeep
+*.mp4
+

README.md

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+---
+license: cc-by-nc-sa-4.0
+language:
+- vi
+library_name: pytorch
+pipeline_tag: video-classification
+tags:
+- computer-vision
+- fall-detection
+- pose-estimation
+- mediapipe
+- temporal-convolution
+---
+
+# Phát hiện té ngã từ video dựa trên tư thế cơ thể
+
+Project code hoàn chỉnh theo pipeline:
+
+`Video RGB → MediaPipe (33 điểm khớp) → chuẩn hóa tư thế → cửa sổ 4 giây → Pose-TCN/Pose-GRU → làm trơn → cảnh báo`
+
+Project đã chạy trên bộ UR Fall Detection (URFD). Kết quả cố định với seed 42 trên tập test gồm 41 cửa sổ thuộc 13 video:
+
+| Mô hình | Accuracy | Precision | Recall | Specificity | F1 | ROC-AUC |
+|---|---:|---:|---:|---:|---:|---:|
+| Random Forest | 97,56% | 100,00% | 87,50% | 100,00% | 93,33% | 1,000 |
+| Logistic Regression | 95,12% | 80,00% | 100,00% | 93,94% | 88,89% | 1,000 |
+| Pose-TCN (CUDA) | 95,12% | 80,00% | 100,00% | 93,94% | 88,89% | 1,000 |
+| Pose-GRU (CUDA) | 90,24% | 70,00% | 87,50% | 90,91% | 77,78% | 0,989 |
+
+Đây là kết quả theo **cửa sổ video**, không phải theo người. URFD không cung cấp ánh xạ danh tính đủ rõ để chia subject-independent, vì vậy không dùng bảng trên để tuyên bố khả năng tổng quát hóa lâm sàng.
+
+## Chạy nhanh
+
+Project dùng Python 3.10–3.12. Tạo môi trường và cài thư viện:
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -e '.[dev]'
+```
+
+Để chạy Pose-GRU trên GPU NVIDIA, cài wheel PyTorch phù hợp trước. Máy đã dùng cho kết quả trong project chạy CUDA 12.8:
+
+```bash
+pip install torch --index-url https://download.pytorch.org/whl/cu128
+python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"
+```
+
+Chạy lại toàn bộ thí nghiệm URFD. Lệnh sẽ tự tải khoảng 70 video từ nguồn chính thức nếu chưa có dữ liệu:
+
+```bash
+python scripts/run_all.py
+```
+
+Chạy demo trên video:
+
+```bash
+python scripts/demo_video.py \
+  --input data/videos/fall/fall-01/fall-01-cam0.mp4 \
+  --model artifacts/experiments/urfd/pose_tcn/model.pt \
+  --output artifacts/demo_fall01.mp4
+```
+
+## Chạy từng bước
+
+```bash
+# 1. Tải 30 video té và 40 video sinh hoạt thường ngày
+python scripts/download_urfd.py
+
+# 2. Trích keypoint và tạo dataset .npz
+python scripts/prepare_dataset.py
+
+# 3. Huấn luyện hai baseline
+python scripts/train_baselines.py
+
+# 4. Huấn luyện hai mô hình sâu
+python scripts/train_gru.py
+python scripts/train_tcn.py
+
+# 5. Tổng hợp bảng và biểu đồ
+python scripts/compare_models.py
+
+# 6. Kiểm thử mã
+pytest -q
+```
+
+Kết quả nằm tại `artifacts/experiments/urfd/`. Mỗi mô hình có trọng số, `metrics.json`, dự đoán từng mẫu, confusion matrix và ROC. File `results.csv` và `model_comparison.png` tổng hợp một lần chạy.
+
+## Dữ liệu riêng
+
+Sắp xếp mỗi video trong một thư mục nhóm riêng. Nhóm có thể là mã người hoặc mã video; nếu có mã người, nên dùng mã người để phép chia không rò rỉ danh tính.
+
+```text
+data/videos/
+├── fall/
+│   ├── person01/video01.mp4
+│   └── person02/video02.mp4
+└── normal/
+    ├── person01/video03.mp4
+    └── person02/video04.mp4
+```
+
+Với dữ liệu không phải URFD và không có annotation frame, mọi cửa sổ trong `fall/` được gán nhãn té. Nên cắt video để mỗi clip té thực sự chứa sự kiện té. Sửa tham số trong [configs/default.yaml](configs/default.yaml).
+
+## Thiết kế thí nghiệm
+
+- Mỗi cửa sổ dài 40 frame ở 10 FPS, tương đương 4 giây; stride 10 frame.
+- Pose được tịnh tiến về tâm hông và chia theo chiều dài thân/độ rộng vai.
+- Pose-GRU dùng 140 chiều/frame: 33 × `(x, y, z, visibility)` và 8 đặc trưng hình học/chuyển động.
+- Pose-TCN dùng 60 chiều/frame: 13 khớp cốt lõi × 4 kênh và 8 đặc trưng bổ sung, giúp giảm overfit trên URFD nhỏ.
+- Chia train/validation/test theo group với tỷ lệ 60/20/20; các cửa sổ cùng video luôn ở cùng tập.
+- Ngưỡng xác suất tối ưu F1 chỉ được chọn trên validation. Test không tham gia chọn mô hình/ngưỡng.
+- GRU và TCN dùng BCE có trọng số lớp, AdamW, early stopping; hai baseline dùng cùng dữ liệu và phép chia.
+
+## Chạy lặp nhiều seed
+
+Lệnh sau chạy cả bốn mô hình với 5 seed, mỗi seed có phép chia theo video riêng. Pose-GRU và Pose-TCN tự dùng CUDA nếu có; hai mô hình scikit-learn chạy CPU.
+
+```bash
+python scripts/run_repeated_experiments.py --seeds 13 21 42 84 123 --device cuda
+```
+
+Kết quả từng lần nằm trong `artifacts/experiments/urfd_repeated/seed_*/`. Bảng tổng hợp mean, standard deviation, min và max nằm tại `aggregate_results.csv`.
+
+Kết quả trên 5 seed đã chạy:
+
+| Mô hình | Accuracy mean ± std | Recall mean ± std | F1 mean ± std | ROC-AUC mean ± std |
+|---|---:|---:|---:|---:|
+| Random Forest | 0,968 ± 0,026 | 0,885 ± 0,114 | **0,907 ± 0,061** | 0,997 ± 0,007 |
+| Logistic Regression | **0,969 ± 0,021** | **0,955 ± 0,062** | 0,904 ± 0,095 | 0,991 ± 0,015 |
+| Pose-TCN (CUDA) | 0,961 ± 0,040 | 0,915 ± 0,132 | 0,901 ± 0,069 | 0,983 ± 0,035 |
+| Pose-GRU (CUDA) | 0,942 ± 0,043 | 0,865 ± 0,139 | 0,842 ± 0,098 | 0,996 ± 0,006 |
+
+URFD có 70 chuỗi (30 fall, 40 ADL), phát hành theo CC BY-NC-SA 4.0 cho nghiên cứu phi thương mại tại [trang dữ liệu chính thức](https://fenix.ur.edu.pl/~mkepski/ds/uf.html). Khi sử dụng, trích dẫn bài của Kwolek và Kępski, DOI `10.1016/j.cmpb.2014.09.005`.
+
+Repository công khai chứa code, checkpoint và kết quả thí nghiệm. Video URFD gốc, pose dataset đã trích và video demo không được đóng gói cùng repository; dùng `scripts/download_urfd.py` để tải dữ liệu từ nguồn chính thức.

artifacts/experiments/.gitkeep

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