Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Аналіз ходи за допомогою DTW× | Безмаркерний захоплення руху× | |
|---|---|---|
| Галузь | Біомеханіка | Біомеханіка |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1978 | 2017 |
| Автор методу≠ | Sakoe and Chiba | Zhe Cao |
| Тип≠ | Sequence alignment and pattern matching | Deep learning pipeline |
| Основоположне джерело≠ | Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49. DOI ↗ | Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI ↗ |
| Інші назви | DTW, Gait pattern matching, Temporal gait comparison | Marker-free tracking, Vision-based motion capture, Deep learning pose estimation |
| Пов'язані | 3 | 3 |
| Підсумок≠ | Dynamic Time Warping (DTW) is a sequence alignment algorithm that measures similarity between time series of different lengths by allowing flexible temporal matching. Applied to gait analysis, DTW enables comparison of walking patterns across subjects and conditions despite variations in cadence or stride length. | Markerless motion capture infers the 3D positions and joint angles of a moving subject from video sequences using computer vision and machine learning. Pioneered by deep learning approaches such as OpenPose and MediaPipe, it eliminates the need for reflective markers or inertial sensors, making motion capture accessible and practical for real-world applications. |
| ScholarGateНабір даних ↗ |
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