Estimation of Ankle Joint Moment From Plantar Pressure Through an Optimized Sensor Layout Using Genetic Algorithm and Deep Forest Regression.

Objective: Ankle joint moments are critical in gait analysis, with accurate assessments typically necessitating complex inverse dynamics modeling. Pressure insoles are widely used wearable devices that have shown feasibility in estimating joint angles. However, achieving cost-effective, high-precision estimation of ankle joint moment remains challenging. This study combines genetic algorithm (GA) with deep forest regression (DFR) to optimize the number and layout of plantar pressure sensors, and estimate ankle joint moment based on plantar pressure.

Methods: 26 healthy young participants were recruited to collect motion trajectories, ground reaction forces, and plantar pressure data while walking at fast, medium, and slow speeds. Ten gait cycles per speed per participant were analyzed for ankle joint moments using inverse dynamics, constituting the dataset. An optimization algorithm was constructed by combining GA with DFR, using the fitness function as the objective for sensor number and layout optimization. The leave-one-out cross-validation was employed to evaluate the precision of the model.

Results: The highest fitness was achieved with an optimized layout using 9 sensors. The Pearson Correlation Coefficients for the sagittal, coronal, and transverse plane moments were 0.967 ± 0.014, 0.918 ± 0.027, and 0.894 ± 0.073. The optimized layout showed no significant difference in estimation accuracy across various walking speeds (P > 0.05).

Conclusion: The proposed GA-DFR algorithm is capable of estimating ankle joint moment accurately and optimizing the number and layout of sensors.

Significance: The algorithm and optimized sensor layout enables the accurate and rapid estimation of ankle joint moment from plantar pressure insoles with trade-off approach.