UPDRS Label Assignment by Analyzing Accelerometer Sensor Data Collected from Conventional Smartphones

Abstract

The study of the characteristics of hand tremors of the patients suffering from Parkinson’s disease (PD) offers an effective way to detect and assess the stage of the disease’s progression. During the semi-quantitative evaluation, neurologists label the PD patients with any of the (0–4) Unified Parkinson’s Diseases Rating Scale (UPDRS) score based on the intensity and prevalence of these tremors. This score can be bolstered by some other modes of assessment as like gait analysis to increase the reliability of PD detection. With the availability of conventional smartphones with a built-in accelerometer sensor, it is possible to acquire the 3-axes tremor and gait data very easily and analyze them by a trained algorithm. Thus we can remotely examine the PD patients from their homes and connect them to trained neurologists if required. The objective of this study was to investigate the usability of smartphones for assessing motor impairments (i.e. tremors and gait) that can be analyzed from accelerometer sensor data. We obtained 98.5% detection accuracy and 91% UPDRS labeling accuracy for 52 PD patients and 20 healthy subjects. The result of this study indicates a great promise for developing a remote system to detect, monitor, and prescribe PD patients over long distances. It will be a tremendous help for the older population in developing countries where access to a trained neurologist is very limited. Also, in a pandemic situation like COVID-19, patients from developed countries can be benefited from such a home-oriented PD detection and monitoring system.