مقایسهٔ روش‌ها

	تحلیل گام با استفاده از پیچش زمانی پویا (DTW)	ثبت حرکت بدون نشانگر
حوزه	بیومکانیک	بیومکانیک
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	1978	2017
پدیدآور	Sakoe and Chiba	Zhe Cao
نوع	Sequence alignment and pattern matching	Deep learning pipeline
منبع بنیادین	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.	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).
نام‌های دیگر	DTW, Gait pattern matching, Temporal gait comparison	Marker-free tracking, Vision-based motion capture, Deep learning pose estimation
مرتبط	3	3
خلاصه	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.