Unsupervised activity recognition with user's physical characteristics data

This paper proposes an activity recognition method that models an end user's activities without using any la-beled/unlabeled acceleration sensor data obtained from the user. Our method employs information about the end user's physical characteristics such as height and gender to find other users whose sensor data prepared in advance may be similar to those of the end user. Then, we model the end user's activities by using the labeled sensor data from the similar users. Therefore, our method does not require the end user to collect and label her training sensor data. We confirmed the effectiveness of our method by using 100 hours of sensor data obtained from 40 participants, and achieved a good recognition accuracy almost identical to that of a recognition method employing an end user's labeled training data.