Kavli Affiliate: Jing Wang
| First 5 Authors: Kuangen Zhang, Jiahong Chen, Jing Wang, Xinxing Chen, Yuquan Leng
| Summary:
Recognizing human locomotion intent and activities is important for
controlling the wearable robots while walking in complex environments. However,
human-robot interface signals are usually user-dependent, which causes that the
classifier trained on source subjects performs poorly on new subjects. To
address this issue, this paper designs the ensemble diverse hypotheses and
knowledge distillation (EDHKD) method to realize unsupervised cross-subject
adaptation. EDH mitigates the divergence between labeled data of source
subjects and unlabeled data of target subjects to accurately classify the
locomotion modes of target subjects without labeling data. Compared to previous
domain adaptation methods based on the single learner, which may only learn a
subset of features from input signals, EDH can learn diverse features by
incorporating multiple diverse feature generators and thus increases the
accuracy and decreases the variance of classifying target data, but it
sacrifices the efficiency. To solve this problem, EDHKD (student) distills the
knowledge from the EDH (teacher) to a single network to remain efficient and
accurate. The performance of the EDHKD is theoretically proved and
experimentally validated on a 2D moon dataset and two public human locomotion
datasets. Experimental results show that the EDHKD outperforms all other
methods. The EDHKD can classify target data with 96.9%, 94.4%, and 97.4%
average accuracy on the above three datasets with a short computing time (1
ms). Compared to a benchmark (BM) method, the EDHKD increases 1.3% and 7.1%
average accuracy for classifying the locomotion modes of target subjects. The
EDHKD also stabilizes the learning curves. Therefore, the EDHKD is significant
for increasing the generalization ability and efficiency of the human intent
prediction and human activity recognition system, which will improve
human-robot interactions.
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