WEARABLE GAIT DEVICE FOR LONG-TERM MONITORING

Abstract

This study describes a low-cost and easy to deploy gait monitoring system that uses an ESP32 microcontroller and an ICM-20948 module. The ESP32 microcontroller collects data from the ICM-20948 module and these data are used to train a convolutional neural network (CNN) to classify gait patterns into two categories: normal and pathological. The results show that the system can achieve a high accuracy for binary gait classification, being able to correctly classify 97.05% of the normal gait samples and 84.54% of the pathological gait samples. The power consumption of the devive was measured using a calibrated and dual-acquisition digital multimeter. The estimated operating time was around 12 hours, with a battery capacity of 1800 mAh LiPo type. Therefore, it could be used to track the gait of patients with neurological disorders or to assess the effectiveness of gait rehabilitation treatments.