Abstract
Motion sensor data collected using Sage Bionetwork's mPower application on the Apple iPhone to record participant activities is analyzed to classify samples as positive or negative for Parkinson's Diagnosis. Pre-processing of the data showed differences in the time and frequency dimensions for features derived from Apple Core motion data. Several classic machine learning classification algorithms were trained on seventy-seven derived data points for best precision, recall, and F-1 score. Accuracy as high as ninety-two percent were achieved, with the best results attained from decision tree and multi-layered artificial neural network algorithms. This research shows that motion data produced on the Apple iPhone using the mPower application shows promise as an accessible platform to classify participants for presence of Parkinson's Disease signs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.