Optimization of Body Pressure Relief Support Wearable Devices Integrating 3D Printing and Gait Recognition Algorithms

To improve wearing comfort and achieve individual recognition, this study designs an ankle exoskeleton that simulates natural human movement based on the joint structure of the human lower limbs. The function of the sole spring is achieved through compression springs on the exoskeleton framework coupled with the foot, and a customized insole is designed using 3D printing technology. This study uses a gait recognition algorithm based on a convolutional gated recurrent unit fully convolutional network with a dual attention mechanism to achieve individual recognition. The results showed that compared to the natural state, when walking with exoskeletons, the integrated electromyographic signals of the gastrocnemius and tibialis anterior muscles decreased by 5.4% and 3.6%, respectively, and the intelligent insole reduced plantar pressure to a certain extent. The accuracy of the proposed gait recognition algorithm could reach 95.26%, which was 2.03% higher than that of fully convolutional networks. In addition, the fuzzy output signals of the left and right feet were combined to obtain the proportions of single support phase and double support phase during walking, which were 92.7% and 7.3%, respectively. This study indicates that a body pressure reducing support wearable device that integrates 3D printing and gait recognition algorithms can reduce lower limb joint pressure, providing a new possibility for improving wearing comfort and achieving individual recognition. It also helps to improve the quality of life for the target audience.