Wearable Accelerometer Layout Optimization for Activity Recognition Based on Swarm Intelligence and User Preference

Wearable intelligent systems that recognize the daily activities of humans have significantly contributed to many useful applications. However, in practical wearable applications, the target individuals may have different body conditions and demands in terms of sensor wearing. Sensors may return different information depending on their placement, which to some extent determines the quality of a recognition system. Obtaining the optimal sensor positions with the highest recognition accuracy plays a significant role in activity recognition design. To contribute to a flexible and user-friendly wearable sensor layout, this paper designed a multistage and multiswarm discrete particle swarm optimization algorithm to explore the best sensor combinations and accuracy trends corresponding to different requirements of sensor numbers. The proposed optimization scheme is applied to investigate the influences that different numbers and placements of wearable accelerometers have on an activity recognition system. Furthermore, to address the issue of user preference regarding the sensor position, a relevant sensor layout can also be designed based on the demands and physical condition of the subject. The proposed method can determine the best sensor position (combinations) with lower computational cost for various activity recognition systems.