Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 510640, China.
The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510630, China.
J Neuroeng Rehabil. 2020 Apr 28;17(1):58. doi: 10.1186/s12984-020-00687-1.
Compensations are commonly observed in patients with stroke when they engage in reaching without supervision; these behaviors may be detrimental to long-term functional improvement. Automatic detection and reduction of compensation cab help patients perform tasks correctly and promote better upper extremity recovery.
Our first objective is to verify the feasibility of detecting compensation online using machine learning methods and pressure distribution data. Second objective was to investigate whether compensations of stroke survivors can be reduced by audiovisual or force feedback. The third objective was to compare the effectiveness of audiovisual and force feedback in reducing compensation.
Eight patients with stroke performed reaching tasks while pressure distribution data were recorded. Both the offline and online recognition accuracy were investigated to assess the feasibility of applying a support vector machine (SVM) based compensation detection system. During reduction of compensation, audiovisual feedback was delivered using virtual reality technology, and force feedback was delivered through a rehabilitation robot.
Good classification performance was obtained in online compensation recognition, with an average F1-score of over 0.95. Based on accurate online detection, real-time feedback significantly decreased compensations of patients with stroke in comparison with no-feedback condition (p < 0.001). Meanwhile, the difference between audiovisual and force feedback was also significant (p < 0.001) and force feedback was more effective in reducing compensation in patients with stroke.
Accurate online recognition validated the feasibility of monitoring compensations using machine learning algorithms and pressure distribution data. Reliable online detection also paved the way for reducing compensations by providing feedback to patients with stroke. Our findings suggested that real-time feedback could be an effective approach to reducing compensatory patterns and force feedback demonstrated a more enviable potential compared with audiovisual feedback.
在未经监督的情况下进行上肢伸展时，脑卒中患者通常会出现代偿行为，这些行为可能会对其长期功能恢复产生不利影响。自动检测和减少代偿有助于患者正确完成任务，促进上肢更好地恢复。
我们的首要目标是验证使用机器学习方法和压力分布数据在线检测代偿的可行性。第二个目标是研究视听或力反馈是否可以减少脑卒中幸存者的代偿。第三个目标是比较视听和力反馈在减少代偿方面的效果。
8 名脑卒中患者在记录压力分布数据的同时进行了上肢伸展任务。评估了支持向量机（SVM）补偿检测系统的离线和在线识别准确率，以验证其可行性。在减少代偿的过程中，使用虚拟现实技术提供视听反馈，通过康复机器人提供力反馈。
在线补偿识别中获得了良好的分类性能，平均 F1 评分超过 0.95。基于准确的在线检测，与无反馈相比，实时反馈显著降低了脑卒中患者的代偿行为（p<0.001）。同时，视听反馈和力反馈之间也存在显著差异（p<0.001），力反馈在减少脑卒中患者代偿方面更有效。
准确的在线识别验证了使用机器学习算法和压力分布数据监测代偿的可行性。可靠的在线检测为脑卒中患者提供反馈，为减少代偿行为铺平了道路。我们的研究结果表明，实时反馈可能是减少代偿模式的有效方法，力反馈比视听反馈具有更大的潜力。
