Description:
<jats:p>This study aimed to detect fall risk behaviors using radar—a non-contact sensor—to prevent falling accidents, which are one of the most fatal problems faced by older adults. Hospitals and nursing homes often have patients who cannot move alone without caregivers. In this context, the process of a patient sitting up from a lying-down position shortly before standing up has been observed as a fall risk behavior. This study added movement information as a new characteristic feature to the range and velocity information used in conventional radarbased behavior recognition studies. Performance comparisons confirmed that the addition of movement information performs excellently in detecting risk situations. Furthermore, a bidirectional long short-term memory model was trained using a feature to predict risk situations. The model exhibited accuracy, recall, and precision rates of 93.84%, 88.57%, and 99.07%, respectively. Additionally, its performance in detecting fall risk behavior was verified by conducting experiments involving continuous behaviors.</jats:p>