Time Series Segmentation Applied to a New Data Set for Mobile Sensing of Human Activities

Human activity recognition (HAR) systems implement workflows that automatically detect activities from motion data, captured e.g. by wearable devices such as smartphones. These devices contain multiple sensors that record human motion as acceleration, rotation and orientation in long time series (TS) data. As a first step, HAR methods typically partition such recordings into smaller subsequences before applying feature extraction and classification. In this study, we evaluate the performance of 6 classical and recently published TS segmentation (TSS) algorithms on a new large HAR benchmark of 126 TS with up to 13 different activities, called MOSAD, recorded with 6 participants using ordinary smartphone sensors. Our results show that the ClaSP algorithm achieves significantly more accurate results compared to the other methods, scoring the best segmentations in 57 out of 126 TS. The FLOSS algorithm also shows promising results, particularly for long TS with many segments. MOSAD is freely available at https://github.com/ermshaua/mobile-sensing-human-activity-data-set.