Vibration-based gait analysis via instrumented buildings
dc.contributor.author | Kessler, Ellis | en |
dc.contributor.author | Malladi, Vijaya V.N. Sriram | en |
dc.contributor.author | Tarazaga, Pablo Alberto | en |
dc.contributor.department | Mechanical Engineering | en |
dc.date.accessioned | 2020-02-03T17:44:46Z | en |
dc.date.available | 2020-02-03T17:44:46Z | en |
dc.date.issued | 2019-09-11 | en |
dc.description.abstract | Gait analysis is an invaluable tool in diagnosing and monitoring human health. Current techniques often rely on specialists or expensive gait measurement systems. There is a clear space in the field for a simple, inexpensive, quantitative way to measure various gait parameters. This study investigates if useful quantitative gait parameters can be extracted from floor acceleration measurements produced by the input of foot falls. A total of 17 participants walked along a 115-ft-long hallway while underfloor mounted accelerometers measured the vertical acceleration of the floor. Signal-energy-based algorithms detect the heel strike of each step during trials. From the detected footsteps, gait parameters such as the average stride length, the time between steps, and the step signal energy were calculated. In this study, a single accelerometer was shown to be enough to detect steps over a 115-ft corridor. Distributions for all gait parameters measured were generated for each participant, showing a normal distribution with low standard deviation. The success of gait analysis using underfloor accelerometers presents possibilities in the widespread adaptation of gait measurements. The ease of installation and operation offers an opportunity to gather long-term gait measurements. Such data will augment current gait diagnostic approaches by filling the gaps between specialist visits. | en |
dc.description.sponsorship | The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge the support received through the John R. Jones III Faculty Fellowship. This work was supported in part by the National Science Foundation via grant no. DGE-1545362, UrbComp (Urban Computing): Data Science for Modeling, Understanding, and Advancing Urban Populations. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. | en |
dc.format.extent | 11 pages | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.doi | https://doi.org/10.1177/1550147719881608 | en |
dc.identifier.issue | 10 | en |
dc.identifier.uri | http://hdl.handle.net/10919/96677 | en |
dc.identifier.volume | 15 | en |
dc.language.iso | en | en |
dc.publisher | Sage | en |
dc.rights | Creative Commons Attribution 4.0 International | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
dc.subject | Floor vibrations | en |
dc.subject | stride length | en |
dc.subject | swing time | en |
dc.subject | step size | en |
dc.subject | smart infrastructure | en |
dc.subject | human gait | en |
dc.title | Vibration-based gait analysis via instrumented buildings | en |
dc.title.serial | International Journal of Distributed Sensor Networks | en |
dc.type | Article - Refereed | en |
dc.type.dcmitype | Text | en |
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