Detecting Anomalies of Daily Living of the Elderly Using Radar and Self-Comparison Method

Along with the aging society, the elderly population increases. Most non-disabled elderly prefer to age in their comfortable homes. To support such home care for the elderly, continuous real-time monitoring of all this and early warning in the event of an unexpected event are beneficial. Current monitoring systems, such as wearable sensors or webcams, could monitor the activity of elderly people and support their independent living. However, it malfunctions when the elderly do not wear wearable sensors; the webcam has privacy concerns. The study proposes a novel intelligent system to monitor the daily life of the elderly and to notify anomalies in real time. Millimeter-wave (mmWave) radar, machine learning, and self-comparison method were adopted to implement such a system. A data-driven self-comparison scheme is proposed to reduce false alarms. Clinical data from 73 seniors (58 males; mean age and standard deviation 71.7 ± 7.4 years; 15 females; 70.8 ± 7.8 years) were collected in the hospital for the training of the sleep prediction model. Five older solidary volunteers attended the data collection at their home for indoor tracking and sleep monitoring. The experimental results revealed that the proposed system could achieve a false alarm rate below 5%. The findings of the study may serve as a guide for the research and development of non-invasive sensing systems for the care of elderly adults at home.