Self-supervised Learning for Accelerometer-based Human Activity Recognition: A Survey

Self-supervised learning (SSL) has emerged as a promising alternative to purely supervised learning, since it can learn from labeled and unlabeled data using a pre-train-then-fine-tune strategy, achieving state-of-the-art performances across many research areas. The field of accelerometer-based human activity recognition (HAR) can benefit from SSL since unlabeled data can be collected cost-efficiently due to the ubiquitous nature of sensors embedded in smart devices, which is in contrast to labeled data, that require a costly annotation process. Motivated by the success of SSL and the lack of surveys on SSL for HAR, this survey comprehensively examines 52 SSL methods applied to HAR, and categorizes them into four SSL paradigms based on pre-training objectives. We discuss SSL strategies, evaluation protocols, and utilized datasets. We highlight limitations in current methodologies, including little large-scale pre-training, the absence of foundation models, as well as the scarcity of systematic domain shift experiments and domain knowledge utilization. Notably, the diversity in evaluation protocols across papers poses a considerable challenge when comparing methods. Future directions outlined in this survey include the development of an SSL framework for HAR to enable standardized benchmarking and large-scale pre-training, along with integrating domain knowledge to enhance model performance.