Real Time Detection of Freezing of Gait of Parkinson Patients based on Machine Learning Running on a Body Worn Device

Abstract

In this work, we present a system that detects Freezing of Gait Detection (FOG) that uses of a single wearable inertial sensor to automatically evaluate a Parkinson’s patient’s gait instability and detect FOG in real-time. A wearable vibrator is our cueing system which is triggered by the FOG detection whenever a FOG episode takes place. The vibration impulses help the patient to prevent FOG by switching to voluntarily movement execution. Sensor data were collected from nine patients with Parkinson’s disease performing Unified Parkinson’s Disease Rating Scale (UPDRS) test under the supervision of a clinical expert. Along with data recording, a video was taken from patient’s parkour. The data were labeled through the recorded video of the patient’s tests and FOG and non-FOG data were assigned. A machine learning model using a deep Long-Short-Term-Memory (LSTM) employ the accelerometer data from the sensor and the inference leads to a FOG or non-FOG classification. The FOG detection model is pruned, quantized and used for real-time inference on Google Coral board worn on the patient’ body. The model deployed on a Google coral board sends a trigger to the cuing device right after the FOG detection and the patient get alert for the happening FOG. The individualized model for one-second windows applied in this work performed an average of 91.5% of sensitivity and 86.5% specificity for models running on PC and 91.7% of sensitivity and 86.7% of specificity for the models tested on Google coral with the latency of 50 millisecond on real-time testing.