Piezoresistive plantar pressure sensors and CNN-based body weight and load estimation.

Abstract

Monitoring user weight, including body weight and afforded load, is crucial for post-fracture rehabilitation. Inappropriate weight levels can delay recovery and increase re-fracture risk. In recent years, insole sensor systems have proven effective in monitoring gait parameters, including plantar pressure and gait cycles. Among all gait parameters, plantar pressure is particularly useful for monitoring and predicting user weight due to its strong correlation. However, previous studies were limited in scenarios and accuracy. To address these issues, this study proposes a piezoresistive plantar pressure sensor system (PPS) integrated with a CNN model. The system uses 96 piezoresistive force sensors to collect plantar pressure data from 107 subjects in both walking and standing conditions with varying loads (0 kg, 5 kg, 10 kg, 15 kg). The data is input into the CNN model for user weight prediction. Results show standing without load achieves an R² of 0.9997 and relative error of 0.0027, while walking with load shows the lowest R² of 0.8857 and relative error of 0.0416. This work enables accurate user weight estimation and supports gait-based healthcare research, particularly in relation to plantar pressure.