{'fraction_non_alphanumeric': 0.025906735751295335, 'fraction_numerical': 0.0, 'mean_word_length': 5.0390625, '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}}
Design and Implementation of a Real-Time Upper Limbs Dyskinesia Detection System

In this paper a Real-Time L-dopa-Induced Dyskinesia (LID) Detection System based on Machine Learning Algorithms (MLAs) and simple devices such as smartphone and smartwatch is presented. The implementation of this system was performed in three steps. Firstly, the data collection is carried out, where each patient wears the smartwatch and completes some tasks, while a smartphone application captures data. Secondly, features in time and frequency domain were extracted from smartwatch data and used as input for the training of different off-line MLAs. Lastly, the best algorithm found has been integrated into a mobile App in order to real-time monitor the smartwatch data and detect LID.