The current motion interaction model has the problems of insufficient motion fidelity and lack of self-adaptation to complex environments. To address this problem, this study proposed to construct a human motion control model based on the muscle force model and stage particle swarm, and based on this, this study utilized the deep deterministic gradient strategy algorithm to construct a motion interaction control model based on the muscle force model and the deep reinforcement strategy. Empirical analysis of the human motion control model proposed in this study revealed that the joint trajectory correlation and muscle activity correlation of the model were higher than those of other comparative models, and its joint trajectory correlation was up to 0.90, and its muscle activity correlation was up to 0.84. In addition, this study validated the effectiveness of the motion interaction control model using the depth reinforcement strategy and found that in the mixed-obstacle environment, the model’s desired results were obtained by training 1.1 × 10³ times, and the walking distance was 423 m, which was better than other models. In summary, the proposed motor interaction control model using the muscle force model and deep reinforcement strategy has higher motion fidelity and can realize autonomous decision making and adaptive control in the face of complex environments. It can provide a theoretical reference for improving the effect of motion control and realizing intelligent motion interaction.

With the rapid expansion of the smart clothing market, designers face increasing pressure to balance functional performance, material suitability, environmental impact, and development efficiency. Conventional design workflows and rule-based assistance methods often struggle to provide adaptive and data-driven support for multi-constraint decision-making. To address this issue, this study proposes an AI-driven decision support system for sustainable smart clothing design based on a multi-scale dynamic graph convolutional network (MDGCN). The proposed system integrates material properties, environmental indicators, and user-oriented design requirements into a unified decision-support framework and further enhances feature extraction through an attention mechanism. Two datasets, the Wearable Technology Material Properties Dataset (WTMPD) and the Environmental Impact Assessment Dataset (EIAD), were used to validate the model and system effectiveness. Experimental results showed that the MDGCN-based model achieved accuracies of 0.964 and 0.943, with recalls of 0.923 and 0.920 on the WTMPD and EIAD datasets, respectively. In system-level evaluation, the proposed decision support system reduced design time from 120 h to 60 h, improved material selection accuracy to 90.2%, and achieved superior operational performance in terms of resource utilization (77.45%), energy consumption (115.25 kWh), and response time (1.56 s). These results demonstrate that the proposed framework can effectively support complex design decision-making while improving efficiency, sustainability, and adaptability in smart clothing development. The study provides a practical AI-enabled system innovation approach for sustainable smart clothing design by linking flexible material selection, environmental impact prediction, and designer-oriented decision support. In addition, the prototype deployment demonstrates the feasibility of applying the proposed system as a design-stage wearable AI tool for mediating human, technological, and environmental considerations in smart clothing development.

Accurately predicting the muscle strength of key muscle–tendon units during human motion is vital for understanding movement mechanisms, optimizing exercise training, evaluating rehabilitation progress, and advancing prosthetic control technologies. Traditional prediction methods often suffer from low accuracy and high computational complexity. To address these challenges, this study employs independent component analysis (ICA) to predict the muscle strength of tendon units in primary moving parts of the human body. The proposed method had the highest accuracy in localization, at 98% when the sample size was 20. When the sample size was 100, the proposed method had the shortest localization time, with a localization time of 0.025 s. The accuracy of muscle strength prediction based on backpropagation neural network for key muscle–tendon units in human motion was the highest, with an accuracy of 99% when the sample size was 100. The method can effectively optimize the accuracy and efficiency of muscle strength prediction for key muscle–tendon units in human motion and reduce computational complexity.

This study proposes a health and performance optimization framework based on Particle Swarm Optimization (PSO) to improve the structural performance and energy utilization efficiency of coastal wind–wave energy harvesting systems. A semi-submersible floating wind turbine–wave energy integrated system is selected as the case study. The control variable method and numerical simulations are employed to determine the optimal structural parameters. Furthermore, a multi-scenario coordinated optimization model for wind, wave, energy storage, and load is established using the Velocity Pause Particle Swarm Optimization (VPPSO) algorithm. The optimal structural parameters are identified as an outer diameter of 16 m, an inner diameter of 8 m, a height of 8 m, and a draft of 3.5 m. The results show that VPPSO achieves faster convergence and better optimization performance compared to conventional algorithms. In the optimal scenario with wind–wave curtailment and energy storage participation, the minimum economic cost of 1780 CNY is achieved after 200 iterations. The proposed method provides theoretical guidance for the optimal design and efficient operation of coastal wind–wave energy harvesting systems.

Improving indoor acoustic comfort in buildings remains challenging for conventional acoustic design methods because they often rely on empirical formulas, static databases, and limited adaptability to dynamic environmental changes. To address these limitations, this study proposes a physics-informed Transformer framework for indoor acoustic comfort optimization in buildings. The proposed framework integrates measured, simulated, and publicly available acoustic data to construct an adaptive dataset for indoor acoustic modeling. A hierarchical time–frequency attention mechanism is introduced to capture complex acoustic features, while a physics-informed module is incorporated to improve the physical consistency of the predicted acoustic responses. In addition, a dynamic optimization strategy is developed to adjust acoustic parameters under changing indoor conditions while considering both acoustic performance and energy efficiency. Experimental results show that the proposed framework achieves a Mel-spectral distortion of 6.12 dB and a speech intelligibility score of 89.3%, while reducing the prediction errors of reverberation time and early decay time to 0.11 s and 0.09 s, respectively. In practical deployment tests, the proposed method increased the early sound energy ratio by 46.4%, improved reverberation time control accuracy by 41.2%, reduced total energy consumption by 29.7%, and increased the user acoustic comfort score by 38.1%, with an average response time of 1.8 s. These results demonstrate that the proposed framework provides an effective data-driven and physics-informed approach for improving indoor acoustic comfort and energy-efficient acoustic management in buildings.