Parkinson's Disease Detection Using Deep learning approach based on Wearable Sensor-Based Daily Monitoring

Abstract

Parkinson's disease (PD) is a movement disorder characterized by motor dysfunction commonly bradyphemia, tremor, rigidity, akinesia, or slowness of movement. Noting that motor states can fluctuate in PD the primary aim of this current paper was to differentiate multiple states using wearable sensors in the patients and detection of this PD based on deep learning (CNN). Methodology: In this paper, the researchers recorded the signals of the accelerometer and gyroscope fixed on the wrist of PD in their regular daily functioning after using this dataset collection. The deep learning architecture developed was to optimize a CNN for analyzing the sensor data for motor status like rest, tremor, and dyskinesia. Results and Conclusion: The availability of high accuracy in defining different degrees of motor state was achieved through the deep learning algorithm hence providing a high option in monitoring pd symptoms. These outcomes also show and suggest the possibility and practicality of the wearable sensor for daily activity monitoring for the identification of PD. It can also provide appropriate information to patients other than doctors regarding their sickness which gives them a platform to effectively handle Parkinson’s disease patients. The error has been determined to be within the vicinity of 98%.