Collaborative classification for daily activity recognition with a smartwatch

Research of daily activity recognition has been extensively conducted in the field of ubiquitous computing. However, previous daily activity recognition schemes are either obtrusive or inaccurate since they use just special-purpose devices. In this paper, we propose the collaborative classification for recognizing daily activities with a smartwatch. We exploit a single off-the-shelf smartwatch to distinguish 5 different daily activities such as eating, vacuuming, sleeping, showering, and TV watching. More precisely, we conduct experiments for collecting sensor data from accelerometer and acoustic sensor which are embedded in a smartwatch. However, the simple combination of the raw acceleration and acoustic data does not deliver accurate recognition accuracy. In order to achieve high accuracy, we propose a collaborative classification algorithm which integrates sensor data and ground-truth label for improving recognition accuracy by constructing a mapping table. We evaluate accuracies using single-sensor based approach, multi-sensor based approach, and our collaborative classification approach. The results from activity recognition for about 20 hours data collected by subjects show reliable accuracies for all 5 activities, and the overall accuracy of our collaborative approach is about 91.5%. Experimental results reveal that our approach improves the recall rate of each activity by up to 21.5% as compared to that of the simply combined multi-sensor based approach.