{'fraction_non_alphanumeric': 0.013665594855305467, 'fraction_numerical': 0.0, 'mean_word_length': 5.417525773195877, '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}}
Machine Learning-Based Physical Activity Tracking with a view to Frailty Analysis

Frailty in old age is defined as the individual intrinsic susceptibility of having bad outcomes following a health problem. It relies on sarcopenia, mobility and activity. Recognizing and monitoring a range of physical activities is a necessary step which precedes the analysis of this syndrome. This paper investigates the optimal tools for this recognition in terms of type and placement of wearable sensors. Two machine learning procedures are proposed and compared on a public dataset. The first one is based on deep learning, where feature extraction is done manually, by constructing activity images from raw signals and applying convolutional neural networks to learn optimal features from these images. The second one is based on shallow learning, where hundreds of handcrafted features are extracted manually, followed by a novel feature selection approach to retain the most discriminant subset.Clinical relevance— This analysis is an indispensable prerequisite to develop efficacious way in order to identify people with frailty using sensors and moreover, to take on the challenge of frailty prevention, an actual world health organization priority.