Abstract
A preliminary study result predicting fall events in patients with Parkinson's disease (PD) by using a simple motion sensor is described in this paper. Causes of falls in people with PD can be postural instability, freezing of gait, festinating gait, dyskinesias, visuospatial dysfunction, orthostatic hypotension, and posture problems. This study uses only one motion sensor in collecting data. Thus, only fall events caused by festinating gait factors, which are moments when the patient suddenly moves faster with smaller steps, can be performed and tested. In this preliminary study, fall event scenarios of simulated test cases are performed by five healthy young subjects aged 20 to 28 years old. The acceleration mode in the motion sensor provides information that can detect how fast the subjects move. Data collected by the sensor will be analyzed by simple analysis methods and machine learning techniques classification. The proposed study achieved an accuracy of 70.3% for the 10-class model, while for binary classification, the accuracy was 99%. Clinical Relevance-This study focuses on predicting falls by analyzing the gaits prior to an actual so that fall prediction can be possible. If falls can be predicted, researchers can develop other protective gear to prevent fall-related injuries not only for PD patients but also for the elderly.