Deep Learning based Gait Abnormality Detection using Wearable Sensor System

Gait is an extraordinary complex function of human body that involves the activation of entire visceral nervous system, making human gait definite to various functional abnormalities. Diagnosis and treatment of such disorders prior to their development can be achieved through integration of modern technologies with state-of-the-art developed methods. Modern machine learning techniques have outperformed and complemented the use of conventional statistical methods in bio-medical systems. In this research a wearable sensor system is presented, which combines plantar pressure measurement unit and Inertial Measurement Units (IMU's) integrated with a stacked Long short-term memory (LSTM) model to detect human gait abnormalities that are prone to the risk of fall. The computed metrics and gait parameters show significant differences between normal and abnormal gait patterns. Three specific abnormalities involving Hemiplegic, Parkinsonian and Sensory-Ataxic gaits are simulated to validate the proposed model and show promising results. The proposed research aims to demonstrate how advanced technologies can be used in gait diagnosis and treatment assistant systems.