Learning Hand Kinematics for Parkinson's Disease Assessment Using a Multimodal Sensor Glove.

ABSTRACT: Hand dysfunctions in Parkinson's disease include rigidity, muscle weakness, and tremor, which can severely affect the patient's daily life. Herein, a multimodal sensor glove is developed for quantifying the severity of Parkinson's disease symptoms in patients' hands while assessing the hands' multifunctionality. Toward signal processing, various algorithms are used to quantify and analyze each signal: Exponentially Weighted Average algorithm and Kalman filter are used to filter out noise, normalization to process bending signals, K-Means Cluster Analysis to classify muscle strength grades, and Back Propagation Neural Network to identify and classify tremor signals with an accuracy of 95.83%. Given the compelling features, the flexibility, muscle strength, and stability assessed by the glov

SUBMITTER: Li Y 

PROVIDER: S-EPMC10369271 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature