Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life.

Self-supervised learning methods have received much attention in skeleton-based human action recognition. These methods rely on pretext tasks to utilize unlabeled data and learn an effective feature encoder. In this paper, a novel self-supervised learning method is proposed. First, we design a new pretext task called view enhanced jigsaw puzzle (VEJP) to improve the learning difficulty of the encoder. The VEJP introduces multi-view information into the jigsaw puzzle, thus forcing the encoder to learn view-independent high-level features of human skeletons. Based on the encoder trained by VEJP, we propose the view pooling encoder (VPE) to integrate the information of multiple views with the pooling mechanism, and the features extracted by VPE are more robust and distinguishable. In addition, by adjusting the difficulty of VEJP, the influence of the pretext task difficulty on the downstream task performance is studied, and the experimental results show that the pretext tasks should be moderately difficult to achieve effective feature learning. Our method achieves competitive results on representative benchmark datasets. It provides a strong baseline for the jigsaw puzzle task and shows advantages in situations where the number of labeled data is minimal.