Finding a person with a wearable acceleration sensor using a 3D position tracking system in daily environments

Person identification with accurate position information is essential for providing location-based services in real environments, such as a shopping mall. For this purpose, we propose a method that integrates 3D position information from environmental depth sensors and acceleration data from wearable devices to anonymously gather the trajectories of people who have wearable devices as well as others. Our proposed method identifies a person who has a wearable device by comparing two time series of acceleration data from device and position information. To do this, we extracted the behaviours of each axis using the changes of each bit of acceleration data at certain time periods. We evaluated our method with data collected at a shopping mall and a children’s playroom to investigate its effectiveness and robustness in different environments. Our evaluation results showed that it achieved an average identification of 85%, which is better than several alternative methods.