Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-34778-8_9
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dc.titleA real-time on-chip algorithm for IMU-based gait measurement
dc.contributor.authorZhu, S.
dc.contributor.authorAnderson, H.
dc.contributor.authorWang, Y.
dc.date.accessioned2013-07-04T08:23:10Z
dc.date.available2013-07-04T08:23:10Z
dc.date.issued2012
dc.identifier.citationZhu, S.,Anderson, H.,Wang, Y. (2012). A real-time on-chip algorithm for IMU-based gait measurement. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7674 LNCS : 93-104. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-34778-8_9" target="_blank">https://doi.org/10.1007/978-3-642-34778-8_9</a>
dc.identifier.isbn9783642347771
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41253
dc.description.abstractThis paper presents a real-time and on-chip gait measurement algorithm used in our Gait Measurement System (GMS). Our GMS is a small foot-mounted device based on an Inertial Measurement Unit (IMU), which contains an accelerometer and a gyroscope. The GMS can compute spatio-temporal gait parameters in real-time and transmit them to a remote receiver. Measured gait parameters include cadence, velocity, stride length, swing/stance ratio and so on. The algorithm is optimized to run in a ATmega328 microprocessor with only 2kB data memory. During a walking session, each stride is recognized instantaneously, and the stride length and other parameters are computed at the same time. Although inexpensive components are utilized, the algorithm achieves high accuracy, with an average stride length error smaller than 3%, and error in total walking distance less than 2%. © 2012 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-34778-8_9
dc.sourceScopus
dc.subjectAlgorithm
dc.subjectGait Measurement
dc.subjectIMU
dc.subjectOn-Chip
dc.subjectReal-Time
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-34778-8_9
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume7674 LNCS
dc.description.page93-104
dc.identifier.isiutNOT_IN_WOS
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