Deep Learning Approach for Gait Detection for Precise Stimulation of FES to Correct Foot Drop.

Automatic detection of foot lift is one of the most important events of Functional Electrical Stimulation (FES). The FES system is used for the correction of Foot Drop (FD). FD is a condition where a person is unable to lift their foot from the ground due to complications that may arise after a stroke or spinal cord injury. It is crucial to accurately detect the patient’s foot lift event when correcting FD through FES as the pulse should only be applied when the person lifts their foot. The FES system applies the electrical pulse based on the input of the foot-lift detection sensor. A conventional FES system employs a sensor that is affixed on the heel to detect the lifting of the foot, but the connecting cables make the patient uncomfortable. To address this problem, IMU (Inertial Measurement Unit)-based sensors have been used, but they have some disadvantages, such as false triggering, low accuracy, and calibration. In this paper, we have presented an algorithm for detecting foot-lift events with high accuracy using a single IMU sensor through the application of deep learning techniques. We have recorded data from 10 healthy people and 10 foot drop patients. We have implemented Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), and Convolutional Neural Network (CNN) models on these data and compared the results of these three models. The proposed algorithm aims to improve the precision of stimulation in the FES system. [ABSTRACT FROM AUTHOR]