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In production, poor ergonomic environments not only lead to increased workloads and health hazards for employees but also tend to reduce efficiency and quality. Recently, the human-cyber-physical (HCP) system has been proposed and widely studies to meet human capabilities and limitations. However, most existing frameworks are still not adaptive enough to integrate humans into a smart production environment due to a lack of real-time individual human factors digitalization. This research proposes an HCP workstation model for comprehensive assembly resources digitalization and autonomous interaction by CPS enabling technologies. Based on this, an adaptive control system has been developed for proactive ergonomic risk mitigation. Computer vision is deployed for real-time individual ergonomic evaluation and a prototype has been set up for functional validation.

Human activities recognition (HAR) and human intent recognition (HIR) are important for medical diagnosis and human-robot interaction. HAR and HIR usually rely on the signals of some wearable sensors, such as inertial measurement unit (IMU), but these signals may be user-dependent, which degrades the performance of the recognition algorithm on new subjects. Traditional supervised learning methods require labeling signals and training specific classifiers for each new subject, which is burdensome. To deal with this problem, this paper proposes a novel non-adversarial cross-subject adaptation method called Gaussian-guided feature alignment (GFA). The proposed GFA metric quantifies the discrepancy between the labeled features of source subjects and the unlabeled features of target subjects so that minimizing the GFA metric leads to the alignment of the source and target features. The GFA metric is estimated by calculating the divergence between the feature distribution and Gaussian distribution, as well as the mean squared error of the mean and variance between source and target features. This paper analytically proves the effect of the GFA metric and validates its performance using three public human activity datasets. Experimental results show that the proposed GFA achieves 1% higher target classification accuracy and 0.5% lower variance than state-of-the-art methods in case of cross-subject validation. These results indicate that the proposed GFA is feasible for improving the generalization of the HAR and HIR.