{'fraction_non_alphanumeric': 0.013513513513513514, 'fraction_numerical': 0.0, 'mean_word_length': 5.2924528301886795, '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}}
Improving the accuracy of wearable activity classifiers

Providing context awareness in wearables helps the user be more efficient in his tasks while being interrupted by the wearable device only when it is needed. In this work, we focus on one important aspect of context awareness in wearables which is activity classification. First, a wearable activity classifier is improved and applied to a medical application. Afterwards, more techniques are proposed that can be used to improve the results' accuracy of any activity classifier. Future research aims to incorporate more context awareness domains that can interact with and help wearable activity classifiers.