A Washing Machine is All You Need? On the Feasibility of Machine Data for Self-Supervised Human Activity Recognition

Learning representations via self-supervision has emerged as a powerful framework for deriving features for automatically recognizing activities using wearables. The current de-facto protocol involves performing pre-training on (large-scale) data recorded from human participants. This requires effort as recruiting participants and subsequently collecting data is both expensive and time-consuming. In this paper, we investigate the feasibility of an alternate source of data for its suitability to lead to useful representations, one that requires substantially lower effort for data collection. Specifically, we examine whether data collected by affixing sensors on running machinery, i.e., recording non-human movements/vibrations can also be utilized for self-supervised human activity recognition. We perform an extensive evaluation of utilizing data collected on a washing machine as the source and observe that state-of-the-art methods perform surprisingly well relative to when utilizing large-scale human movement data, obtaining within 5-6 % Fl-score on some target datasets, and exceeding on others. In scenarios with limited access to annotations, models trained on the washing-machine data perform comparably or better than end-to-end training, thereby indicating their feasibility and potential for recognizing activities. These results are significant and promising because they have the potential to substantially lower the efforts necessary for deriving effective wearables-based human activity recognition systems.