用两个可穿戴传感器实时检测中风后代偿性躯干动作，精度高且适合临床应用。
Machine Learning-Based Real-Time Detection of Compensatory Trunk Movements Using Trunk-Wrist Inertial Measurement Units
- 仅用躯干和手腕两个部位的惯性传感器，结合机器学习实现检测。
- 模型在真实数据上达到0.80的宏观F1值，支持实时处理。
- 方法简单可靠，适合康复训练中长期监测，对临床推广有潜力。
中风后常出现代偿性躯干运动（CTMs），导致异常运动模式，影响康复效果。现有检测方法多依赖复杂设备，难以实现实时监测。本研究探索使用两个惯性测量单元（IMU）配置，结合机器学习实现可靠、实时的CTM检测。实验采集了10名健康受试者在模拟障碍条件下（肘部支具限制屈伸、阻力带诱发屈肌协同模式）进行日常活动的数据，以光学运动捕捉（OMC）和人工标注视频为参考。通过系统的位置缩减分析，确定腕部与躯干运动学为最小但充分的传感位置。采用极端梯度提升分类器（XGBoost），经留一被试者交叉验证，双IMU模型表现优异：宏平均F1 = 0.80 ± 0.07，MCC = 0.73 ± 0.08，ROC-AUC > 0.93，性能接近基于OMC的模型，且预测延迟满足实时需求。可解释性分析表明，躯干动态及腕-躯干交互特征起主导作用。初步在4名神经功能障碍患者数据上测试，模型仍保持一定区分能力（ROC-AUC ~ 0.78），但阈值依赖性表现不稳定，揭示临床泛化挑战。结果表明，稀疏可穿戴传感是实现治疗与日常生活中实时监测CTMs的可行路径。
原文摘要 · Abstract (English)
Compensatory trunk movements (CTMs) are commonly observed after stroke and can lead to maladaptive movement patterns, limiting targeted training of affected structures. Objective, continuous detection of CTMs during therapy and activities of daily living remains challenging due to the typically complex measurements setups required, as well as limited applicability for real-time use. This study investigates whether a two-inertial measurement unit configuration enables reliable, real-time CTM detection using machine learning. Data were collected from ten able-bodied participants performing activities of daily living under simulated impairment conditions (elbow brace restricting flexion-extension, resistance band inducing flexor-synergy-like patterns), with synchronized optical motion capture (OMC) and manually annotated video recordings serving as reference. A systematic location-reduction analysis using OMC identified wrist and trunk kinematics as a minimal yet sufficient set of anatomical sensing locations. Using an extreme gradient boosting classifier (XGBoost) evaluated with leave-one-subject-out cross-validation, our two-IMU model achieved strong discriminative performance (macro-F1 = 0.80 +/- 0.07, MCC = 0.73 +/- 0.08; ROC-AUC > 0.93), with performance comparable to an OMC-based model and prediction timing suitable for real-time applications. Explainability analysis revealed dominant contributions from trunk dynamics and wrist-trunk interaction features. In preliminary evaluation using recordings from four participants with neurological conditions, the model retained good discriminative capability (ROC-AUC ~ 0.78), but showed reduced and variable threshold-dependent performance, highlighting challenges in clinical generalization. These results support sparse wearable sensing as a viable pathway toward scalable, real-time monitoring of CTMs during therapy and daily living.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。