Wearable Device for Observation of Physical Activity with the Purpose of Patient Monitoring Due to COVID-19

Abstract

In late 2019, a new genre of coronavirus (COVID-19) was first identified in humans in Wuhan, China. In addition to this, COVID-19 spreads through droplets, so quarantine is necessary to halt the spread and to recover physically. This modern urgency creates a critical challenge for the latest technologies to detect and monitor potential patients of this new disease. In this vein, the Internet of Things (IoT) contributes to solving such problems. This paper proposed a wearable device that utilizes real-time monitoring to detect body temperature and ambient conditions. Moreover, the system automatically alerts the concerned person using this device. The alert is transmitted when the body exceeds the allowed temperature threshold. To achieve this, we developed an algorithm that detects physical exercise named “Continuous Displacement Algorithm” based on an accelerometer to see whether a potential temperature rise can be attributed to physical activity. The people responsible for the person in quarantine can then connect via nRF Connect or a similar central application to acquire an accurate picture of the person’s condition. This experiment included an Arduino Nano BLE 33 Sense which contains several other sensors like a 9-axis IMU, several types of temperature, and ambient and other sensors equipped. This device successfully managed to measure wrist temperature at all states, ranging from 32 °C initially to 39 °C, providing better battery autonomy than other similar devices, lasting over 12 h, with fast charging capabilities (500 mA), and utilizing the BLE 5.0 protocol for data wireless data transmission and low power consumption. Furthermore, a 1D Convolutional Neural Network (CNN) was employed to classify whether the user is feverish while considering the physical activity status. The results obtained from the 1D CNN illustrated the manner in which it can be leveraged to acquire insight regarding the health of the users in the setting of the COVID-19 pandemic.

Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data

“…A more recent systematic review has shown that PA improves several health outcomes, including all-cause mortality and cardiovascular diseases [ 2 ]. More recent works also show the benefit of combining PA with other clinically relevant measures, such as temperature, to gain insight into a patient’s health status [ 3 ]. As a measure of PA, step counts derived from digital devices have gained increasing popularity as an objective measure to replace self-reported data [ 4 , 5 , 6 ].…”

The SI4CARE Project: Using Wearable Devices for Assisting People with Dementia in Calabria

“…Since older people may present severe side effects and have difficulties moving (absence of public transportation and living in remote areas), there is a need to introduce advanced telemonitoring systems. In recent works, the effectiveness of telemonitoring some physiological parameters has been demonstrated [4,[7][8][9]. The project aims twofold: supporting the families and people with dementia, and showing the efficacy of telemonitoring in a real scenario within the SI4CARE project.…”

Measuring of the Energy Expenditure during Balance Training Using Wearable Electronics

“…The use of such complex methods is therefore impossible for the intended purpose. Instead, however, it is possible and very easy to use wearable electronics, also called "wearables" [15,16]. Patients with balance disorders are at a higher risk of falling and decompensating their health condition than the healthy population.…”