Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Abstract

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.

Monitoring the skin biophysical parameters among coronavirus patients for three days in a row: a preliminary study

“…However, this does not mean that no patient had a high temperature, since several of the patients had an elevated skin temperature on the second day, which might be due to inflammation. In addition, it is reported that fever is one of the prominent clinical symptoms of novel coronavirus patients [20]. Moreover, the decrease in temperature (3 rd day) may be due to the patients being given antipyretic drugs as a part of the hospital treatment protocol.…”

Machine learning applications for COVID-19 outbreak management

“…Zhang, Zhu [105] presented a wearable device with inertial and temperature sensors that may be utilized to apply human behavior recognition (HAR) to body surface temperature detection and correction during COVID-19. The sensing device used a microcontroller, a 6-axis sensor, and a temperature sensor to collect data from ten different users in four distinct daily activity situations.…”

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

“…IoT devices are able to measure specific human physiological parameters such as heart rate (HR), respiration rate (RR) and blood pressure (BP), electrocardiogram (ECG), Electroencephalogram (EEG), electromyography (EMG), body temperature, Oxygen levels, blood glucose (BG), and the volume of air inspired and expired by lungs (Swayamsiddha and Mohanty, 2020;Chakkor et al, 2021b;Zhang et al, 2021), so that the early diagnosis and necessary treatment can be given One proposed system leverages IoT devices as part of its architecture to actively monitor users data and send this data to heath care centers for evaluation. Also, there are relatively new measurements that monitor the users' heart rate, number of daily steps and sleep time to scan pre-symptomatic cases of COVID-19 for the same purpose-early diagnosis and timely necessary treatment (Josephine et al, 2020;Lo and Sim, 2021;Ni et al, 2021b;Dadon et al, 2022).…”

“…Detection and monitoring (Arun et al, 2020;Choyon et al, 2020;Dhadge and Tilekar, 2020;Ding et al, 2020;Hossain et al, 2020;Josephine et al, 2020;Karthi and Jayakumar, 2020;Seshadri et al, 2020;Swayamsiddha and Mohanty, 2020;Al-Emran and Ehrenfeld, 2021;Anjali et al, 2021;Ashwin et al, 2021;Awotunde et al, 2021Awotunde et al, , 2022Chakkor et al, 2021a,b;Elbasi et al, 2021;Lo and Sim, 2021;Lubecke et al, 2021;Mukati et al, 2021;Mumtaz et al, 2021;Ni et al, 2021b;Peladarinos et al, 2021;Rehman et al, 2021;Tan et al, 2021;Uddin et al, 2021;Zhang et al, 2021;Dadon et al, 2022;Goergen et al, 2022) Contact tracing Indoor contact tracing (Jeong et al, 2019;Dmitrienko et al, 2020;Kumar et al, 2022;Li et al, 2020b;Otoom et al, 2020;Schmidtke, 2020;Thangamani et al, 2020;Abueg et al, 2021;Lo and Sim, 2021;Mcheick et al, 2021;Wojtusiak et al, 2021) Outdoor contact tracing (Elbir et al, 2020;Maghdid and Ghafoor, 2020;Ting et al, 2020;…”