Feature Engineering for Activity Recognition from Wrist-worn Motion Sensors

With their integrated sensors, wrist-worn devices, such as smart watches, provide an ideal platform for human activity recognition. Particularly, the inertial sensors, such as accelerometer and gyroscope can efficiently capture the wrist and arm movements of the users. In this paper, we investigate the use of accelerometer sensor for recognizing thirteen different activities. Particularly, we analyse how different sets of features extracted from acceleration readings perform in activity recognition. We categorize the set of features into three classes: motion related features, orientation-related features and rotation-related features and we analyse the recognition performance using motion, orientation and rotation information both alone and in combination. We utilize a dataset collected from 10 participants and use different classification algorithms in the analysis. The results show that using orientation features achieve the highest accuracies when used alone and in combination with other sensors. Moreover, using only raw acceleration performs slightly better than using linear acceleration and similar compared with gyroscope.