School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, P. R. China.
Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Adv Sci (Weinh). 2023 Jul;10(20):e2206982. doi: 10.1002/advs.202206982. Epub 2023 May 7.
Hand dysfunctions in Parkinson's disease include rigidity, muscle weakness, and tremor, which can severely affect the patient's daily life. Herein, a multimodal sensor glove is developed for quantifying the severity of Parkinson's disease symptoms in patients' hands while assessing the hands' multifunctionality. Toward signal processing, various algorithms are used to quantify and analyze each signal: Exponentially Weighted Average algorithm and Kalman filter are used to filter out noise, normalization to process bending signals, K-Means Cluster Analysis to classify muscle strength grades, and Back Propagation Neural Network to identify and classify tremor signals with an accuracy of 95.83%. Given the compelling features, the flexibility, muscle strength, and stability assessed by the glove and the clinical observations are proved to be highly consistent with Kappa values of 0.833, 0.867, and 0.937, respectively. The intraclass correlation coefficients obtained by reliability evaluation experiments for the three assessments are greater than 0.9, indicating that the system is reliable. The glove can be applied to assist in formulating targeted rehabilitation treatments and improve hand recovery efficiency.
帕金森病患者的手部功能障碍包括僵硬、肌肉无力和震颤，这些症状会严重影响患者的日常生活。为此，我们开发了一种多模态传感器手套，用于量化患者手部帕金森病症状的严重程度，同时评估手部的多功能性。在信号处理方面，我们使用了各种算法来量化和分析每个信号：使用指数加权平均算法和卡尔曼滤波器来滤除噪声，归一化处理弯曲信号，使用 K-Means 聚类分析来对肌肉力量等级进行分类，使用反向传播神经网络来识别和分类震颤信号，准确率为 95.83%。鉴于其出色的性能，手套评估的灵活性、肌肉力量和稳定性与临床观察结果高度一致，kappa 值分别为 0.833、0.867 和 0.937。通过可靠性评估实验获得的这三个评估的组内相关系数均大于 0.9，表明该系统具有较高的可靠性。该手套可用于辅助制定有针对性的康复治疗方案，提高手部恢复效率。
