Optimal Placement of Foot Pressure Sensors for Lower-Limb Exoskeletons Based on Multi-Objective Particle Swarm Optimization Algorithm.

This study proposes an optimization method for sensor placement in lower-limb exoskeleton robots, aiming to improve the accuracy of the Center of Pressure (CoP) estimation during various human activities. Utilizing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the proposed method identifies an optimal sensor count, as the Root Mean Square Error (RMSE) decelerates when the sensor count exceeds 6. Application of this method resulted in a reduction of the Mean Absolute Error (MAE) to 4.13 mm and 8.92 mm on the mediolateral and anteroposterior axes respectively, a 22.8% improvement in CoP estimation accuracy compared to traditional anatomical methods. Further analysis revealed that weight parameters influence the CoP estimation accuracy, suggesting an enhancement in sensor placement efficiency through the adjustment of individual objectives' significance. The proposed sensor placement optimization method is expected to significantly enhance the performance of lower-limb exoskeleton robots and increase user satisfaction. Moreover, it could substantially contribute to the enhancement of human-computer interaction in robot control by providing a more accurate reflection of the user's intentions. These findings highlight the importance of continuing research in the field of lower-limb exoskeleton robots.