A UWB-Radar-Based Adaptive Method for In-Home Monitoring of Elderly

Qimeng Li, Jikui Liu, Raffaele Gravina, Weilin Zang, Ye Li, Giancarlo Fortino

Abstract: The healthcare industry faces challenges due to rising treatment costs, an aging population, and limited medical resources. Remote monitoring technology offers a promising solution to these issues. This article introduces an innovative adaptive method that deploys an ultrawideband (UWB) radar-based Internet of Medical Things (IoMT) system to remotely monitor elderly individuals’ vital signs and fall events during their daily routines. The system employs edge computing for prioritizing critical tasks and a combined cloud infrastructure for further processing and storage. This approach enables monitoring and telehealth services for elderly individuals. A case study demonstrates the system’s effectiveness in accurately recognizing high-risk conditions and abnormal activities, such as sleep apnea and falls. The experimental results show that the proposed system achieved high accuracy levels, with a mean absolute error (MAE) ± standard deviation of absolute error (SDAE) of 1.23± 1.16 bpm for heart rate (HR) detection and 0.22 ± 0.27 bpm for respiratory rate (RR) detection. Moreover, the system demonstrated a recognition accuracy of 90.60% for three types of falls (i.e., stand, bow, squat to fall), one daily activity, and No Activity Background. These findings indicate that the radar sensor provides a high degree of accuracy suitable for various remote monitoring applications, thus enhancing the safety and well-being of elderly individuals in their homes.