基于骨骼动作预测与关节级性能评估的自主远程康复

中文摘要 AI 辅助

自主康复系统不仅需要识别人体动作，还需提供结构化反馈以在无需治疗师持续监督的情况下支持用户。本文提出一种远程康复流程，该流程将基于骨骼的动作质量评估与短期动作预测整合为双模块系统，可在无标记的RGB视频上运行。自注意力双向LSTM采用MMD-NCA度量学习执行动作质量分类，而基于图的动作预测模块计算预测姿态与观测姿态之间的关节位置误差，生成空间定位的偏差信号。每个模块均在已建立的基准上独立评估：分类器在PROZIS数据集的深蹲序列上实现96.45%的平均类准确率，所采用的STARS预测器在Human3.6M数据集上560ms时的平均MPJPE为75.8mm，在所有预测时域上均优于图基线与循环基线。该框架旨在最终部署于辅助机器人及居家康复场景，端到端整合与临床验证是未来工作的重要方向。通过在单一系统中整合动作识别与预测，本研究为自主、反馈驱动的远程康复迈出了一步，以实现更具可及性与可扩展性的康复解决方案。

英文摘要

Autonomous rehabilitation systems must not only recognize human motion but also provide structured feedback to support users without continuous therapist supervision. This paper presents a telerehabilitation pipeline that integrates skeleton-based exercise quality assessment and short-term motion prediction into a two-module system operating on marker-free RGB video. A self-attentive Bidirectional LSTM performs exercise quality classification using MMD-NCA metric learning, while a graph-based motion prediction module computes per-joint position errors between predicted and observed poses, generating spatially localized deviation signals. Each module is evaluated independently on established benchmarks: the classifier achieves 96.45% mean-class accuracy on squat sequences from the PROZIS dataset, and the adopted STARS predictor achieves a mean MPJPE of 75.8 mm at 560 ms on Human3.6M, outperforming graph and recurrent baselines across all prediction horizons. The framework is designed for eventual deployment in assistive robotics and home-based rehabilitation contexts; end-to-end integration and clinical validation are important directions for future work. By combining motion recognition and prediction in a single system, this work contributes a step toward autonomous, feedback-driven telerehabilitation, for more accessible and scalable rehabilitation solutions.

发表机构

• Institute of Systems and Robotics（系统与机器人研究所）
• University of Coimbra（科英布拉大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Accepted at IEEE RO-MAN2026