Müller J, Weiß A, Eskofier B (2025)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2025
Publisher: Springer Nature Switzerland AG 2026
Edited Volumes: Artificial Intelligence in Healthcare
Series: Lecture Notes in Computer Science
City/Town: Gewerbestrasse 11, CH‑6330 Cham, Schweiz
Book Volume: 16038
Pages Range: 227–241
ISBN: 978-3-032-00652-3
DOI: 10.1007/978-3-032-00652-3
We present a personalized, pose-estimation-based physiotherapy system that delivers real-time movement feedback using 3D pose estimation and efficient frame-level pose matching from monocular video. The pipeline builds on the MotionAGFormer framework, combining a lightweight YOLO-based person detector, HRNet for 2D keypoint detection, and a hybrid transformer-graph convolution network for 3D pose lifting. To align patient movements with reference sequences, we employ a local dynamic time warping (DTW) algorithm constrained by a Sakoe-Chiba band, reducing computational complexity by narrowing the search space, thereby enabling speed-accuracy trade-offs. Evaluation on the 3DFit dataset under varying noise levels shows that small bands maintain accuracy under low noise but degrade with increasing noise, while larger bands recover accuracy close to global DTW. Notably, the smallest tested band achieves a 4.8×" role="presentation" style="box-sizing: inherit; display: inline-block; line-height: normal; font-size-adjust: none; word-spacing: normal; overflow-wrap: normal; text-wrap-mode: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;"> speed-up over global DTW. To support individualized rehabilitation, we propose an adaptive audio-visual feedback strategy with a user interface that dynamically adjusts tolerance bounds around target joint angles based on time-dependent performance. This enables continuous, joint-level feedback and fine-grained movement correction. Together, these components form a scalable, interpretable, and adaptive biofeedback system suitable for both clinical settings and home-based digital physiotherapy.
APA:
Müller, J., Weiß, A., & Eskofier, B. (2025). Adaptive Biofeedback for Digital Physiotherapy Using Sakoe-Chiba Constrained Pose Matching. In Daniele Cafolla, Timothy Rittman, Hao Ni (Eds.), Artificial Intelligence in Healthcare. (pp. 227–241). Gewerbestrasse 11, CH‑6330 Cham, Schweiz: Springer Nature Switzerland AG 2026.
MLA:
Müller, Jonas, Alexander Weiß, and Björn Eskofier. "Adaptive Biofeedback for Digital Physiotherapy Using Sakoe-Chiba Constrained Pose Matching." Artificial Intelligence in Healthcare. Ed. Daniele Cafolla, Timothy Rittman, Hao Ni, Gewerbestrasse 11, CH‑6330 Cham, Schweiz: Springer Nature Switzerland AG 2026, 2025. 227–241.
BibTeX: Download