Diagnosis of Resting Tremor in Parkinson’s Disease Using Accelerometer and Gyroscope Sensors Built into a Smartwatch

Abstract

Monitoring resting tremor in Parkinson’s disease (PD) can be performed using wearable technology and machine learning. Smartwatches offer a cost-effective and non-intrusive way to track tremors remotely. However, to ensure precise monitoring in free-living environments, optimized systems are needed. This chapter discuss about the performance of inertial sensors to identify resting tremors and its classification according to MDS-UPDRS III. Six PD patients wore a smartwatch on their wrists while performing different exercise based on MDS-UPDRS. During eight weeks, data from triaxial accelerometers and gyroscopes were collected simultaneously and analyzed using machine learning techniques. In tremor presence detection, using binary classification, the use of only accelerometer gives the best results in terms of accuracy (97%) and training time (47 s) compared accelerometer and gyroscope combined (96.4% and 67 s) and only gyroscope alone (93% and 59 s). In the MDS-UPDRS scale detection, using multi-class models, the best accuracy is offered by the combination of accelerometer and gyroscope (96.5%) but offers the worst training times (77 s), while accelerometer is slightly worse (96.1%) but require the less training time (57 s). These results show the performance and training times of Machine Learning models for the detection of resting tremor and prediction of the MDS-UPDRS assessment for the correct decision making of sensors and models to be used in future application developments. The results could be used to contribute to the development of reliable tremor monitoring systems using devices equipped with inertial sensors and Machine Learning algorithms