Digital Twin-Enhanced Radar and Joint Communication-Sensing Systems: Application in Accurate Fall Severity Classification and Beyond

Abstract

The growing population of old seniors presents a significant challenge to healthcare systems worldwide. According to the United Nations Population Fund (UNFPA), the number of people aged 65 and older is 10.3% of the global population and is expected to reach 20.7% by 2074. The World Health Organization (WHO) reports that in the near future, by 2023, one in 6 people will be over the age of 60. This increase in the elderly population poses a serious challenge to healthcare providers at retirement homes because of the need to provide individual care to their residents. Falls stand out as the predominant cause of injury and death among the elderly. As stated by the National Council on Aging (NCOA), 1 in 4 Americans aged 65 and older fall each year, which equates to 14 million people. The NCOA also reports that the cost of treating injuries resulting from falls is expected to reach $101 billion by 2030. Moreover, the Center for Disease Control and Prevention (CDC) reports that repeated fall incidents double after falling once. This sets a significant burden on healthcare providers to assess the severity of falls and provide immediate care for those who are in need. In this study, we present a novel approach for fall detection, leveraging radar-based sensing systems as well as joint communication-sensing systems and advanced digital twin simulations. The choice of radar technology is rooted in its capability for high-resolution detection of micro-movements and its inherent respect for individual privacy, as it does not require visual imaging. Moreover, the choice of joint communication-sensing systems is motivated by the growing potential of 5G technology in enabling real-time sensing along with communication. Both systems have the capability for utilizing more physical resources, enabling greater resolution enhancement and more accurate detection. Both systems offer a non-intrusive and privacy-preserving solution for fall detection, ensuring the safety and dignity of the elderly. The integration of digital twins, replicating a diverse array of human physiology and fall dynamics, allows for extensive, varied, and ethical training of sophisticated machine learning algorithms without the constraints and ethical concerns of using human subjects. Our proposed methodology has led to significant advancements in the accuracy and sensitivity of detecting and assessing fall severity, especially in diverse populations and scenarios. We observed notable improvements in the system’s ability to discern subtle variations in falls, a critical factor in elderly care where such incidents can have serious health implications. Our approach not only sets a new benchmark in fall detection technology but also demonstrates the vast potential of combining radar and joint communication-sensing technology with digital simulations in medical research. This research paves the way for innovative patient monitoring solutions, offering a beacon of hope in improving senior care and proactive health management. In this study, the digital twin environment was created for both systems, radar and 5G, to simulate various fall scenarios under different conditions. For both systems, the simulated data was used to train machine learning models to detect the severity of falls, verifying the proposed methodology for severity of fall classification in an ideal environment. Furthermore, the correlation between the simulation and measurement results is presented. Measurement campaigns were conducted for both systems to validate the simulation results and to demonstrate the feasibility of the proposed methodology in real-world scenarios. Employing convolutional neural networks for the radar system, we obtained an accuracy of 99.45% using simulated data and 81.25% using measured data in detecting the severity of falls. The analysis addressed various parameters distinguishing different scenarios, including fall speed and the participant’s body size. On the other hand, for the 5G system, we achieved an accuracy of 92.46% using simulated data and 88.9% using measured data in detecting the severity of falls.