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Optimal Sensor Configuration for Activity Recognition during Whole-body Exercises

机译:全身运动中活动识别的最佳传感器配置

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Advances in wearable devices with inertial measurement units (IMUs) for the detection of different motor activities and monitoring training tasks have important applications in tele-rehabilitation. These technologies can play an effective role in improving the quality of life for people with progressive movement disorders such as Parkinson's disease (PD). Considering cost, simplicity, and practicality, a smaller and more efficient number of IMUs that can accurately recognize the type of movement is preferable. The purpose of the current study was to design an affordable and accurate wearable device with IMUs to detect thirty four different motor activities in a customized training program called LSVT-BIG11Lee Silverman Voice Technique-Big https://www.lsvtglobal.com/LSVtbig[1], which is usually used for people with PD. Nine neurologically healthy individuals performed all 34 tasks. The collected data were processed in windows of 2.5 seconds. Eight features in time and frequency domains and discrete wavelet transforms were calculated. Dimension reduction was performed using the PCA22Principal Component Analysis algorithm. NM33Nearest Mean, RBF44Radial Basis Function, SVM55Support Vector Machine, and k-NN66k-Nearest Neighbors classifiers were then trained and used to recognize the activity. A genetic algorithm was utilized to decide which sensors and signals took part in the classification to produce the best accuracy. Our results showed that the four sensors installed on the left shank, right thigh, left forearm, and right arm provided the optimal number and arrangement to achieve a precision of 94.3% and sensitivity of 93.4% using NM classification. Also, an adaptation algorithm was utilized in order to maintain the quality of recognition for new users.
机译:具有惯性测量单元(IMU)的可穿戴设备的进步,用于检测不同的运动活动并监视训练任务,在远程康复中具有重要的应用。这些技术在改善进行性运动障碍(如帕金森氏病(PD))患者的生活质量方面可以发挥有效作用。考虑到成本,简单性和实用性,最好是可以准确识别运动类型的IMU数量更少,效率更高。本研究的目的是设计一种价格适中且精确的可穿戴设备,带有IMU,以定制的称为LSVT-BIG的培训计划来检测34种不同的运动活动 1 1 Lee Silverman语音技术-大https://www.lsvtglobal.com/LSVtbig[1],通常用于PD患者。九名神经系统健康的人完成了全部34项任务。收集的数据在2.5秒的窗口中进行处理。计算了时域和频域的八个特征以及离散小波变换。使用PCA进行尺寸缩小 2 2 主成分分析算法。 NM 3 3 最近平均,RBF 4 4 径向基函数,SVM 5 5 支持向量机和k-NN 6 6 然后,对k最近邻分类器进行了训练,并用于识别该活动。利用遗传算法来确定哪些传感器和信号参与了分类,以产生最佳的准确性。我们的结果表明,使用NM分类,安装在左腿,右大腿,左前臂和右臂上的四个传感器提供了最佳的数量和布置,以实现94.3%的精度和93.4%的灵敏度。此外,为了适应新用户的识别质量,采用了一种自适应算法。

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