Smart Sensing Systems for the Detection of Human Motion Disorders

Abstract We propose a wearable wireless sensing system for the home monitoring of specific symptoms of the Parkinson's disease. The system is composed of inertial sensors, a station for the real time processing (smartphone/PC/tablet) and dedicated algorithms. The recognition algorithms use fusion of raw signals from accelerometers and gyroscopes. The system provides a robust and reliable detection of the involuntary freezing of gait, which is a common and dangerous symptom of the Parkinson's disease causing falls. The proposed system provides an early detection of the freezing of gate at its outset with excellent performance in terms of sensitivity and precision, and timely provides an audio feedback to the patient for releasing the involuntary block state.