Determining the potential risks of musculoskeletal disorders through working postures in a workplace is expensive and time-consuming. A novel intelligent rapid entire body assessment (REBA) system based on convolutional pose machines (CPM), entitled the Quick Capture system, was applied to determine the risk levels. The aim of the study was to validate the feasibility and reliability of the CPM-based REBA system through a simulation experiment. The reliability was calculated from the differences of motion angles between the CPM-based REBA and a motion capture system. Results show the data collected by the Quick Capture system were consistent with those of the motion capture system; the average of root mean squared error (RMSE) was 4.77 and the average of Spearman’s rho (ρ) correlation coefficient in the different 12 postures was 0.915. For feasibility evaluation, the linear weighted Cohen’s kappa between the REBA score obtained by the Quick Capture system and those from the three experts were used. The result shows good agreement, with an average proportion agreement index (P₀) of 0.952 and kappa of 0.738. The Quick Capture system does not only accurately analyze working posture, but also accurately determines risk level of musculoskeletal disorders. This study suggested that the Quick Capture system could be applied for a rapid and real-time on-site assessment.

Musculoskeletal disorders not only impact workers’ health but also result in significant economic losses to society. Sanitation workers often have to lift waste bags from containers, leading to shoulder joint flexion of 90° or more, exposing them to hazardous environments for extended periods. This study combines deep learning and image recognition to create a Quick Capture Evaluation System (QCES). By comparing body angles captured in the sanitation workers’ work environment with those from OptiTrack motion capture, the system showed an average Root Mean Square Error of 5.64 for 18 different postures, and an average Spearman’s rho of 0.87, indicating its precision. Compared with scores assessed by three experts, the system demonstrated an average Cohen’s kappa of 0.766, proving its reliability. Practical assessments of sanitation workers revealed that tilting the waste containers could significantly improve their posture and reduce the risk of Work-Related Musculoskeletal Disorders. It proves that the QCES system can accurately and rapidly assess the on-site posture of a particular occupation.

Scalp image detection faces challenges such as limited evaluation dimensions, difficulties in quantifying user perception, and insufficient discriminative power of traditional assessment methods. To address these issues, this paper proposes a multi-attribute decision-making model for deep learning algorithm selection. The model integrates subjective and objective weighting through a hybrid approach, where natural language processing (NLP) techniques extract perceptual preferences from user reviews, and the Interval-valued neutrosophic set analytic hierarchy process (IVNS-AHP) and entropy weight method (EWM) are employed to determine subjective and objective weights, respectively. The combined weights are used within the IVNS-VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) framework, enhanced by a possibility distribution (PD) to improve discriminative capability. Experiments were conducted using multiple performance metrics, including Precision, Recall, mean Average Precision at IoU = 0.5 (mAP@50), F1 Score, frames per second (FPS), and Parameters, to evaluate mainstream scalp detection algorithms. The results demonstrate that YOLOv8n achieves the highest comprehensive ranking with strong stability across different decision preferences. Comparative analyses with TODIM (an acronym in Portuguese of interactive and multiple attribute decision-making), TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and fuzzy VIKOR variants confirm that the proposed PD-VIKOR method provides superior ranking stability and discriminative precision, offering a more reliable and robust evaluation under uncertainty.

Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This paper proposes a research approach combining computational fluid dynamics (CFD) and ergonomics to address these issues and enhance the comfort of adolescent skiers. We developed head and face mask models based on the head dimensions of 15–17-year-old males. For enclosed cavities, ensuring the smooth expulsion of exhaled air to prevent re-inhalation is the primary challenge. Through fluid simulation of airflow characteristics within the cavity, we evaluated three different inlet configurations. The results indicate that the location of the air inlets significantly affects the airflow characteristics within the cavity. The side inlet design (type II) showed an average face temperature of 35.35 °C, a 38.5% reduction in average CO₂ concentration within the cavity, and a smaller vortex area compared to the other two inlet configurations. Although the difference in airflow velocity within the cavity among the three configurations was minimal, the average exit velocity differed by up to 0.11 m/s. Thus, we conclude that the side inlet configuration offers minimal obstruction to airflow circulation and better thermal insulation when used in the design of fully enclosed helmets. This enhances the safety and comfort of adolescent wearers during physical activities in cold environments. Through this study, we aim to further promote the development of skiing education, enhance the overall quality of adolescents’ skiing, and thus provide them with more opportunities for the future.

Medical infusion pump is a commonly used clinical nursing equipment, which is of great value in the field of medicine.On one side, this paper collected and analysed user requirements by conducting user research on nurses and patients; on the other hand, it analysed the software and hardware interface, combined with the present market used the medical infusion pump, proposed the design principle of human-computer interaction of medical infusion pump, and the improved design is carried out under this guidance finall for having a better usability and user experience.