An abnormal gait monitoring system for patients with Parkinson's disease based on wearable devices

Gait disturbance is one of the main clinical symptoms of Parkinson's disease, mainly manifested by disrupted gait rhythm and increased variability, prone to bradykinetic, festinating, freezing, and other gaits in different stages of the disease. Wearable devices are widely used tools to monitor gait disorders in Parkinson's disease. This study aims to develop a wearable device-based system for monitoring and quantitatively analyzing multiple abnormal gait patterns, which includes wearable devices, a mobile application, and a server. The wearable device is a combination of a force-sensitive insole and an inertial measurement unit. The mobile application connects to the sensors via Bluetooth to collect the signals and transmits them to the server, which calculates the features and uses a pre-trained machine learning classifier to detect abnormalities in the patient's gait. During model training, a subset of features with 70.01 % importance of all features was retained, and the performance of three machine learning classifiers was compared for normal gait, bradykinetic gait, festinating gait, and freezing of gait, with the best results of 0.9722 recall, 0.9788 precision, 97.31 % accuracy, and 0.9755 F1-score.