A Deep Q Learning Based Method for Optimized Monitoring of Activities of Daily Living U sing a Smart Watch and a Companion Robot

In this paper, we developed an activities of daily living (ADLs) monitoring system using a smart watch and a robot for elderly care. In order to balance the activity recognition accuracy, privacy concerns, robot resource cost and power consumption on the watch during monitoring, we proposed a Deep Q Learning model to solve a sensor selection problem. Based on the above four criteria, the robot runs the Deep Q Learning algorithm to decide whether to activate the robot sensors or the watch sensors for data collection. First, we presented the overview of the ADL monitoring system. Second, we developed the Deep Q Learning algorithm by considering the transition motions as part of the environment states. Third, we created a smart watch application for data collection and communication between the robot and the watch. Finally, the proposed model was trained and evaluated based on both offline data and real time data collected in our smart home testbed. The results showed that the proposed method could recognize ADLs with high accuracy while saving about 14.6% energy compared with the baseline periodic methods.