  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 the target subject but also from each other. The current UDA methods for
HMI recognition ignore the difference between each source subject, which
reduces the classification accuracy. Therefore, this paper considers the
differences between source subjects and develops a novel theory and algorithm
for UDA to recognize HMI, where the margin disparity discrepancy (MDD) is
extended to multi-source UDA theory and a novel weight-aware-based multi-source
UDA algorithm (WMDD) is proposed. The source domain weight, which can be
adjusted adaptively by the MDD between each source subject and target subject,
is incorporated into UDA to measure the differences between source subjects.
The developed multi-source UDA theory is theoretical and the generalization
error on target subject is guaranteed. The theory can be transformed into an
optimization problem for UDA, successfully bridging the gap between theory and
algorithm. Moreover, a lightweight network is employed to guarantee the
real-time of classification and the adversarial learning between feature
generator and ensemble classifiers is utilized to further improve the
generalization ability. The extensive experiments verify theoretical analysis
and show that WMDD outperforms previous UDA methods on HMI recognition tasks.
