College of Information Science and Technology, Donghua University, Shanghai 201620, China.
College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China.
Sensors (Basel). 2021 Nov 12;21(22):7540. doi: 10.3390/s21227540.
Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants' body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.
现有的使用 G 传感器来识别日常活动的可穿戴系统已经在医学、运动和军事等领域得到了广泛的应用，而体温作为人体的一个明显生理特征，在 HAR 的系统设计和相关应用中很少被考虑到。在 COVID-19 常态化的背景下，疫情防控成为重中之重。体温监测在人群发热的初步筛查中起着重要作用。因此，本文提出了一种嵌入惯性和温度传感器的可穿戴设备，用于通过评估识别算法，将人体行为识别（HAR）应用于体表温度检测，实现体温监测和调整。该传感系统由基于 STM 32 的微控制器、6 轴（加速度计和陀螺仪）传感器和温度传感器组成，用于从 10 名个体在 4 种不同日常活动场景下捕获原始数据。然后，通过信号标准化、数据堆叠和重采样对采集的原始数据进行预处理。对于 HAR，实现了几种机器学习（ML）和深度学习（DL）算法来对活动进行分类。为了比较具有温度传感信号的七维数据集上不同分类器的性能，考虑了评估指标和算法运行时间，并发现随机森林（RF）是性能最好的分类器，识别准确率为 88.78%，高于不考虑温度数据的情况（<78%）。此外，实验结果表明，在动态活动中，参与者的体表温度比坐姿时低，这可能与 COVID-19 预防中由于温度偏差而导致可能遗漏发热人群有关。根据不同的个体活动，防疫人员应参考本文提供的正态分布曲线的均值期望和方差的具体值，推断出患者对应的标准正常体温。
