Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan.
Sensors (Basel). 2023 Mar 22;23(6):3354. doi: 10.3390/s23063354.
Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people's activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives. This study proposes an HAR system for classifying 18 types of physical activity using data from sensors embedded in smartphones and smartwatches. The recognition process consists of two parts: feature extraction and HAR. To extract features, a hybrid structure consisting of a convolutional neural network (CNN) and a bidirectional gated recurrent unit GRU (BiGRU) was used. For activity recognition, a single-hidden-layer feedforward neural network (SLFN) with a regularized extreme machine learning (RELM) algorithm was used. The experimental results show an average precision of 98.3%, recall of 98.4%, an of 98.4%, and accuracy of 98.3%, which results are superior to those of existing schemes.
移动健康（mHealth）利用移动设备、移动通信技术和物联网（IoT）不仅改进了传统的远程医疗和监测与警报系统，还改进了日常生活中的健身和医疗信息意识。在过去的十年中，由于人们的活动与他们的身心健康之间存在很强的相关性，因此对人体活动识别（HAR）进行了广泛的研究。HAR 还可用于日常生活中对老年人的护理。本研究提出了一种使用智能手机和智能手表中嵌入式传感器数据对 18 种身体活动进行分类的 HAR 系统。识别过程包括两个部分：特征提取和 HAR。为了提取特征，使用了由卷积神经网络（CNN）和双向门控循环单元（BiGRU）组成的混合结构。对于活动识别，使用具有正则化极端学习机（RELM）算法的单隐层前馈神经网络（SLFN）。实验结果表明，平均精度为 98.3%，召回率为 98.4%，F1 值为 98.4%，准确率为 98.3%，优于现有方案的结果。
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