  Wearable sensor-based human activity recognition (HAR) has emerged as a
principal research area and is utilized in a variety of applications. Recently,
deep learning-based methods have achieved significant improvement in the HAR
field with the development of human-computer interaction applications. However,
they are limited to operating in a local neighborhood in the process of a
standard convolution neural network, and correlations between different sensors
on body positions are ignored. In addition, they still face significant
challenging problems with performance degradation due to large gaps in the
distribution of training and test data, and behavioral differences between
subjects. In this work, we propose a novel Transformer-based Adversarial
learning framework for human activity recognition using wearable sensors via
Self-KnowledgE Distillation (TASKED), that accounts for individual sensor
orientations and spatial and temporal features. The proposed method is capable
of learning cross-domain embedding feature representations from multiple
subjects datasets using adversarial learning and the maximum mean discrepancy
(MMD) regularization to align the data distribution over multiple domains. In
the proposed method, we adopt the teacher-free self-knowledge distillation to
improve the stability of the training procedure and the performance of human
activity recognition. Experimental results show that TASKED not only
outperforms state-of-the-art methods on the four real-world public HAR datasets
(alone or combined) but also improves the subject generalization effectively.
