The medial longitudinal arch is fundamental to weight distribution, balance, and lower limb biomechanics, and its evaluation is important for identifying abnormalities such as flatfoot or high arch. Traditional clinical methods for assessing the foot arch index (FAI) are often constrained by limited accessibility and inconsistent accuracy. To overcome these limitations, this study proposes a deep learning-based plantar pressure measurement system (DLPPMS) designed for accurate and affordable static foot arch evaluation. The system integrates two resistive pressure sensor arrays combined into a 24 × 24 matrix to acquire plantar pressure data in real time. To enhance spatial resolution and improve the fidelity of pressure distribution, Bessel interpolation is employed to generate smooth, high-resolution plantar pressure maps. Deep learning-based pose estimation and instance segmentation models are further applied to isolate the plantar region and identify anatomical keypoints relevant for FAI computation. The system was validated on participants with flatfoot, normal arch, and high arch conditions, demonstrating high segmentation accuracy, reliable keypoint localization, and consistent FAI estimation with minimal error compared to reference values. These results confirm that the DLPPMS provides accurate, repeatable, and low-cost assessment of the medial longitudinal arch under static conditions. Overall, this work highlights the potential of combining pressure sensing, interpolation algorithms, and deep learning into a portable and scalable system, offering promising applications not only for clinical diagnostics but also for biomechanical research, preventive healthcare, and rehabilitation monitoring.

In the development of decarbonization technologies and renewable energy, water electrolysis has emerged as a key technology. The efficiency of hydrogen production and its applications are significantly affected by power stability. Enhancing power stability not only improves hydrogen production efficiency and reduces maintenance costs but also ensures long-term reliable system operation. This study proposes a thermal control method that stabilizes hydrogen output by precisely adjusting the temperature of the electrolysis stack, thereby improving hydrogen production efficiency. Fluctuations in the electrolysis stack temperature can lead to instability in the hydrogen output and energy utilization, negatively affecting overall hydrogen production. To address this issue, this study introduces an innovative system architecture and a novel thermal control strategy combining fuzzy logic control with a long short-term memory neural network. This method predicts and adjusts the flow rate of chilled water to maintain the electrolysis stack temperature within a range of ±1 °C while sustaining a constant power output of 10 kW. This approach is crucial for ensuring system stability and maximizing hydrogen production efficiency. Long-term experiments have validated the effectiveness and reliability of this method, demonstrating that this thermal control strategy not only stabilizes the hydrogen production process but also increases the volume of hydrogen generated.