Wrist-Worn Sensors and Machine Learning for Parkinson’s Disease Detection: Investigation of Binary and Multiclassification Problem

Parkinson's disease (PD) is a disorder that affects the central nervous system and causes severe motor and nonmotor problems. PD is currently cureless, but early diagnosis and proper therapy can slow down its progression. The latest research employs sophisticated experimental setups for PD diagnosis and enables the binary classification of subjects as healthy/PD. However, most are only suitable for clinical settings with limited monitoring capabilities, and their performance still needs improvement. This work detects PD from healthy control (HC) subjects while enabling multiclassification of PD stages using wrist-worn sensors on both hands. It also monitors PD progression. We gathered inertial measurement unit (IMU) data from 85 subjects at a hospital, 55 of whom were diagnosed with various stages of PD using the Hoehn-Yahr scale while performing 11 exercises under the neurologists' supervision. We preprocess the data by downsampling and bandpass filtering. Next, we segment the signals...

Parkinson's disease (PD) is a disorder that affects the central nervous system and causes severe motor and nonmotor problems. PD is currently cureless, but early diagnosis and proper therapy can slow down its progression. The latest research employs sophisticated experimental setups for PD diagnosis and enables the binary classification of subjects as healthy/PD. However, most are only suitable for clinical settings with limited monitoring capabilities, and their performance still needs improvement. This work detects PD from healthy control (HC) subjects while enabling multiclassification of PD stages using wrist-worn sensors on both hands. It also monitors PD progression. We gathered inertial measurement unit (IMU) data from 85 subjects at a hospital, 55 of whom were diagnosed with various stages of PD using the Hoehn-Yahr scale while performing 11 exercises under the neurologists' supervision. We preprocess the data by downsampling and bandpass filtering. Next, we segment the signals into overlapping windows and extract time- and frequency-domain features to input into various machine learning (ML) algorithms. We investigate the impact of the proposed exercises and sensor combinations via ensemble models and transfer learning. The score-based ensemble method achieves an f1-micro of 0.938 for HC/PD using the left-hand sensor, while both sensors can be used to get f1-micro 0.867 for multiclassification tasks. We also found that the left-hand sensor performs better than the right-hand sensor.