{'fraction_non_alphanumeric': 0.018259935553168637, 'fraction_numerical': 0.0010741138560687433, 'mean_word_length': 5.6571428571428575, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Parkinson’s Disease Classification of mPower Walking Activity Participants

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.