Revolutionizing Geriatric Care: Radar and Digital Twin Technology for Fall Severity Detection

Abstract

In this study, we present a novel approach for fall detection, leveraging radar-based 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. 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 technology with digital simulations in medical research. This research paves the way for innovative patient monitoring solutions, offering a beacon of hope in improving seniors care and proactive health management. In this study, diverse fall scenarios were simulated under varied conditions. The correlation between the simulation and measurement results is presented. Employing convolution neural networks, we obtained an accuracy of 99.45% using simulated data and 81.25% using measured data, in detecting severity of falls. The analysis addressed various parameters distinguishing different scenarios, including fall speed and the participant’s body size.