{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90611"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90611","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Good-walk recognition using Android smartphone accelerometer with application on senior patients","abstract":"Good walk from one's everyday activities can be used towards chronic disease diagnosis. Smartphones have become increasingly popular among people across ages. Properties including light weight, computationally powerful make smartphones ideal platforms for activity tracking and analysis. This work focuses on good walk recognition using smartphone accelerometer readings. The algorithms are validated with activity data collected from a large pool of healthy college students and senior patients. Softwares are implemented for walk recognition and pulmonary function evaluations, and are integrated to a pipeline as part of a sequence of activity data analysis.","abstract_html":"Good walk from one's everyday activities can be used towards chronic disease diagnosis. Smartphones have become increasingly popular among people across ages. Properties including light weight, computationally powerful make smartphones ideal platforms for activity tracking and analysis. This work focuses on good walk recognition using smartphone accelerometer readings. The algorithms are validated with activity data collected from a large pool of healthy college students and senior patients. 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