Lou Jingjing, Yang Fan, Lv Changsheng
Guangzhou Institute of Sports Science, Guangzhou 510620, Guangdong, China.
Guangzhou Tianhe Sports Centre, Guangzhou 510620, Guangdong, China.
Comput Intell Neurosci. 2022 Jun 21;2022:5678736. doi: 10.1155/2022/5678736. eCollection 2022.
In order to improve the detection function of wearable intelligent devices in the Internet of things and facilitate people to control a variety of information such as heart rate, exercise state, blood oxygen saturation, and so on, the scientific detection of human physical health based on wearable devices based on Internet of things technology is proposed. Through the combination of software- and hardware-related functional modules, the real-time detection of human physical health information can be effectively realized. Firstly, the detection principle of optical capacitance product pulse wave signal and the waveform characteristics of pulse wave are introduced, and then the application scenarios and advantages of wearable devices are further introduced; then, the convolutional neural network for pulse wave signal denoising and the basic principle of self-encoder are introduced; finally, the regression prediction method and support vector machine method for pulse wave signal feature extraction are introduced in detail. The pulse wave based on optical capacitance product is removed to improve the waveform quality of pulse wave signal. Firstly, the system software development environment is briefly described. Then, the software design of watch terminal master device based on MSP432 and belt terminal slave device based on MSP430 are described in detail, and the detailed program implementation flow of each key technology in the system is given. In addition, the fall detection algorithm based on threshold discrimination is studied, and the program implementation of the algorithm is also described in detail. Finally, the system is tested. The results show that normal state mainly include normal walking, jogging, and fast sitting, and the accuracy rate is 97%, 95%, and 93%, respectively. For fall state, the experimenter needs to simulate various possible fall states, and the accuracy rate is 95%, 93%, and 95%, respectively, which verifies the detection accuracy of the algorithm. The system can automatically turn on the satellite positioning function when the user's physical sign parameters are abnormal or the user's current fall dangerous situation occurs, and send the current position information and alarm content information through the GSM module, so that the dangerous situation can be found and handled at the first time.
为了提升物联网中可穿戴智能设备的检测功能，便于人们掌控心率、运动状态、血氧饱和度等各类信息，提出了基于物联网技术的可穿戴设备对人体身体健康进行科学检测的方法。通过软硬件相关功能模块的结合，能够有效实现对人体身体健康信息的实时检测。首先，介绍了光电容积脉搏波信号的检测原理以及脉搏波的波形特征，进而进一步介绍了可穿戴设备的应用场景及优势；接着，介绍了用于脉搏波信号去噪的卷积神经网络以及自编码器的基本原理；最后，详细介绍了用于脉搏波信号特征提取的回归预测方法和支持向量机方法。去除基于光电容积的脉搏波，以提高脉搏波信号的波形质量。首先简要描述系统软件开发环境。然后详细描述基于MSP432的手表终端主设备和基于MSP430的腰带终端从设备的软件设计，并给出系统中各关键技术的详细程序实现流程。此外，研究了基于阈值判别的跌倒检测算法，并详细描述了该算法的程序实现。最后对系统进行测试。结果表明，正常状态主要包括正常行走、慢跑和快速坐下，准确率分别为97%、95%和93%。对于跌倒状态，实验者需模拟各种可能的跌倒状态，准确率分别为95%、93%和95%，验证了算法的检测准确性。当用户体征参数异常或出现当前跌倒危险情况时，系统可自动开启卫星定位功能，并通过GSM模块发送当前位置信息和报警内容信息，以便能第一时间发现并处理危险情况。
