BACKGROUND Compensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to reduce compensatory trunk movements and improving the motor function of the paretic arm. Sensor-based and camera-based systems have been developed to detect compensatory movements, but they still have some limitations, such as causing object obstructions, requiring complex setups and raising privacy concerns. To overcome these drawbacks, this paper proposes a compensatory movement detection system based on pressure distribution data and is unobtrusive, simple and practical. Machine learning algorithms were applied to classify compensatory movements automatically. Therefore, the purpose of this study was to develop and test a pressure distribution-based system for the automatic detection of compensation movements of stroke survivors using machine learning algorithms. METHODS Eight stroke survivors performed three types of reaching tasks (back-and-forth, side-to-side, and up-and-down reaching tasks) with both the healthy side and the affected side. The pressure distribution data were recorded, and five features were extracted for classification. The k-nearest neighbor (k-NN) and support vector machine (SVM) algorithms were applied to detect and categorize the compensatory movements. The surface electromyography (sEMG) signals of nine trunk muscles were acquired to provide a detailed description and explanation of compensatory movements. RESULTS Cross-validation yielded high classification accuracies (F1-score>0.95) for both the k-NN and SVM classifiers in detecting compensation movements during all the reaching tasks. In detail, an excellent performance was achieved in discriminating between compensation and noncompensation (NC) movements, with an average F1-score of 0.993. For the multiclass classification of compensatory movement patterns, an average F1-score of 0.981 was achieved in recognizing the NC, trunk lean-forward (TLF), trunk rotation (TR) and shoulder elevation (SE) movements. CONCLUSIONS Good classification performance in detecting and categorizing compensatory movements validated the feasibility of the proposed pressure distribution-based system. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to monitor compensation movements automatically by using the pressure distribution-based system when stroke survivors perform seated reaching tasks.

中文翻译：

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使用压力分布数据和机器学习算法检测中风幸存者的补偿运动。

背景技术补偿运动是中风幸存者在就座时通常采用的，并且可能对其长期康复产生不利影响。检测补偿对于指导患者减少补偿性躯干运动并改善仿臂的运动功能很有用。已经开发了基于传感器和基于摄像头的系统来检测补偿运动，但是它们仍然存在一些局限性，例如导致物体阻塞，需要复杂的设置并引起隐私问题。为了克服这些缺点，本文提出了一种基于压力分布数据的补偿运动检测系统，该系统不显眼，简单且实用。应用机器学习算法对补偿运动进行自动分类。所以，这项研究的目的是开发和测试基于压力分布的系统，该系统使用机器学习算法自动检测中风幸存者的补偿运动。方法八名卒中幸存者在健康侧和患侧均进行了三种类型的伸手任务（前后，左右和上下伸手任务）。记录压力分布数据，并提取五个特征进行分类。应用k最近邻（k-NN）和支持向量机（SVM）算法对补偿运动进行检测和分类。采集了九个躯干肌肉的表面肌电图（sEMG）信号，以提供详细的描述和对代偿运动的解释。结果交叉验证产生了较高的分类精度（F1评分> 0。95）中的k-NN和SVM分类器来检测所有到达任务期间的补偿运动。详细地说，在区分补偿和非补偿（NC）运动方面取得了出色的性能，平均F1得分为0.993。对于补偿运动模式的多类分类，在识别NC，躯干前倾（TLF），躯干旋转（TR）和肩部抬高（SE）运动时，平均F1得分为0.981。结论在对补偿运动进行检测和分类中，良好的分类性能验证了所提出的基于压力分布的系统的可行性。