A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition

arXiv:2404.15366v1 Announce Type: cross
Abstract: Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classifier trained on labeled source subjects (domains) performs poorly on unlabeled target subject since the difference in individual motor characteristics. The unsupervised domain adaptation (UDA) method has become an effective way to this problem. However, the labeled data are collected from multiple source subjects that might be different not only from …

abstract arxiv classifier cs.lg difference domain domain adaptation domains eess.sp human natural recognition robot robots type unsupervised