AI RESEARCH
Bio-Inspired Self-Supervised Learning for Wrist-worn IMU Signals
arXiv CS.LG
•
ArXi:2603.10961v1 Announce Type: new Wearable accelerometers have enabled large-scale health and wellness monitoring, yet learning robust human-activity representations has been constrained by the scarcity of labeled data. While self-supervised learning offers a potential remedy, existing approaches treat sensor streams as unstructured time series, overlooking the underlying biological structure of human movement, a factor we argue is critical for effective Human Activity Recognition (HAR). We.