Self-Taught Learning for Activity Spotting in On-body Motion Sensor Data

Activity spotting has shown to be a highly beneficial approach in context recognition, however lacking robustness limits its widespread use. This work introduces the concept of self-taught learning to activity spotting, which is inspired by natural human learning. The self-taught learning concept was adapted for activity spotting, in particular, to make use of unlabeled data, which does not need to include rel-evant pattern events. Thus, the approach can utilise background data (NULL class), for which a large amounts of data often exist. A performance comparison of self-taught and conventional activity spotters showed the potential of this new learning approach. Furthermore, an analysis using reduced amounts of supervised training instances yielded up to ~15% larger performance for the self-taught spotter compared to the conventional one.