This study aimed to enhance the real-time performance and accuracy of vigilance assessment by developing a hidden Markov model (HMM). Electrocardiogram (ECG) signals were collected and processed to remove noise and baseline drift. A group of 20 volunteers participated in the study. Their heart rate variability (HRV) was measured to train parameters of the modified hidden Markov model for a vigilance assessment. The data were collected to train the model using the Baum–Welch algorithm and to obtain the state transition probability matrix $\widehat{A}$ and the observation probability matrix $\widehat{B}$. Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and the Viterbi algorithm was used to find the optimal state, which was compared with the actual state. The constructed vigilance assessment model had a high accuracy rate, and the accuracy rate of data prediction for these three volunteers exceeded 80%. Our approach can be used in wearable products to improve their vigilance level assessment functionality or in other fields that have key positions with high concentration requirements and monotonous repetitive work.

In the field of psychology and cognition within closed cabins, noncontact vital sign detection holds significant potential as it can enhance the user’s experience by utilizing objective measurements to assess emotions, making the process more sustainable and easier to deploy. To evaluate the capability of noncontact methods for emotion recognition in closed spaces, such as submarines, this study proposes an emotion recognition method that employs a millimeter-wave radar to capture respiration signals and uses a machine-learning framework for emotion classification. Respiration signals were collected while the participants watched videos designed to elicit different emotions. An automatic sparse encoder was used to extract features from respiration signals, and two support vector machines were employed for emotion classification. The proposed method was experimentally validated using the FaceReader software, which is based on audiovisual signals, and achieved an emotion classification accuracy of 68.21%, indicating the feasibility and effectiveness of using respiration signals to recognize and assess the emotional states of individuals in closed cabins.

The human-machine function allocation (HFA) strategy of manned submersibles is an important factor that affects the reliability of oceanauts. However, the uncertainty of the HFA strategy makes its optimization very complicated. To this end, a non-cooperative game-based HFA method is proposed, which transforms the multi-objective optimization model of mental workload and situation awareness (SA) under the allocation strategy into a non-cooperative game model and forms a mapping relationship. Mental workload and situation awareness are used as non-cooperative game players. Assignable functions are attributed to the players by fuzzy clustering analysis and combined with non-assignable functions to construct the utility matrix by utility functions. The optimal allocation strategy combination is obtained through the Nash equilibrium analysis of the utility matrix. The proposed method was applied to the optimization of the HFA strategy of the manned submersible during the navigation.

In the design of complex products, change propagation often leads to overlapping impact paths, structural conflicts, and decreased coordination efficiency, especially in multi-source change scenarios. These issues limit the effectiveness of existing path extraction and management methods. This study addresses the challenge of managing multi-source change propagation by proposing a novel optimization method. The method involves constructing a multi-layer propagation network model that integrates structural, functional, and form-level information and combines propagation risk assessment with frequency feedback mechanisms. By employing a path cost function based on node risks and propagation probabilities, the model accurately evaluates the costs of change propagation and identifies high-risk paths while prioritizing those with lower costs. Additionally, a multi-source path combination optimization approach is developed, and it dynamically adjusts path costs to reduce overlap and conflicts, improving coordination between paths in multi-source change scenarios. The proposed approach was validated through a case study based on the design data of an intelligent cabin system, where the constructed propagation network reflects real-world structural, functional, and form-level dependencies. The experimental results show that the proposed method reduces total propagation cost by an average of 27.3% and lowers node-level conflict rate by 41.6% compared with baseline methods. Tests on different complex products confirmed consistent advantages over baselines with $p<{10}^{-5}$, while also reducing redundancy and conflicts. The method maintained these advantages across networks of different sizes with linear scalability. Sensitivity analysis confirms that frequency-feedback weighting $\alpha$ is the main driver for cost reduction, and the overlap penalty $\lambda$ effectively suppresses node congestion. The method provides a robust solution for managing change propagation in complex product design and enhances overall propagation efficiency and system adaptability.

Ergonomic assessments for specialized vehicle cockpits are often costly, subjective, or fragmented. To address these issues, this study proposes and validates a quantifiable comprehensive evaluation method combining optical motion capture with simulation. The methodology uses motion capture to acquire accurate, dynamic operator posture data, which drives a digital human model in a virtual environment. A novel assessment framework then integrates the results from six ergonomic tools into a single, comprehensive score using a multi-criteria weighting model, overcoming the ‘information silo’ problem of traditional software. In a case study optimizing a flatbed transporter cockpit, the method guided a redesign that significantly improved the overall ergonomic score from 0.422 to 0.277. The effectiveness of the optimization was validated by a 40% increase in key control accessibility and a significant reduction in electromyography (EMG) signals in the neck, shoulder, and lumbar regions. This study provides an innovative, data-driven methodology for the objective design and evaluation of customized human–machine systems, demonstrating its utility in reducing physical strain and enhancing operator comfort, with broad applicability to other complex industrial environments.