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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2019,9,9]]},"abstract":"<jats:p>We demonstrate a new foot-mounted sensor system for mobile gait analysis which is based on Ultra Wideband (UWB) technology. Our system is wireless, inexpensive, portable, and able to estimate clinical measurements that are not currently available in traditional Inertial Measurement Unit (IMU) based wearables such as step width and foot positioning. We collect a dataset of over 2000 steps across 21 people to test our system in comparison with the clinical gold-standard GAITRite, and other IMU-based algorithms. We propose methods to calculate gait metrics from the UWB data that our system collects. Our system is then validated against the GAITRite mat, measuring step width, step length, and step time with mean absolute errors of 0.033m, 0.032m, and 0.012s respectively. This system has the potential for use in many fields including sports medicine, neurological diagnostics, fall risk assessment, and monitoring of the elderly.<\/jats:p>","DOI":"10.1145\/3351231","type":"journal-article","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T15:58:26Z","timestamp":1568131106000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["Mobile Gait Analysis Using Foot-Mounted UWB Sensors"],"prefix":"10.1145","volume":"3","author":[{"given":"Boyd","family":"Anderson","sequence":"first","affiliation":[{"name":"NUS Graduate School of Integrative Sciences and Engineering and School of Computing, National University of Singapore, Singapore"}]},{"given":"Mingqian","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore"}]},{"given":"Vincent Y. 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