This paper presents an insole-type sensor module with a novel leaf-inspired force-sensitive resistor (FSR) array for accurate three-dimensional ground reaction force (GRF) estimation during human’s various motions. Joint torque analysis, essential for numerous applications in biomechanics and wearable robotics, necessitates the measurement of three-dimensional GRF vector information, traditionally achieved in indoor environments using costly force plates. To overcome these limitations, this study proposes an alternative method by incorporating FSRs on three inclined planes within the insole. A vector scaling process transforms the force values from the FSRs into the three-dimensional force vector, enabling continuous and user-independent estimation of GRF. The sensor module is integrated with machine learning, demonstrating its accuracy and usability in various motion scenarios. The results confirm the effectiveness of the leaf-inspired FSR array, giving the possibilities for portable and cost-effective motion analysis systems.

Hand gesture recognition has received considerable attention as an intuitive interaction method in recent years. This research introduces a new wearable hand gesture recognition system that employs pneumatic mechanomyography (pMMG) to directly monitor the wrist tendon group, which transmits muscle force to the fingers. The experimental findings demonstrate that the proposed method provides raw observations proportional to the finger flexion force, with highR-squared values exceeding 0.94. The performance of the proposed system was evaluated by conducting a hand gesture experiment consisting of 28 hand gestures. The proposed method achieved an average accuracy of 98.12%, surpassing the surface electromyography (sEMG) system's accuracy of 93.89%. Furthermore, the fusion of pMMG and sEMG sensors yielded an accuracy of 99.18%. The results suggest that the proposed approach exhibits the potential to enhance the accuracy and efficiency of hand gesture recognition systems.