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dc.contributor.authorPatima Silsupadolen_US
dc.contributor.authorPaphawee Prupetkaewen_US
dc.contributor.authorTeerawat Kamnardsirien_US
dc.contributor.authorVipul Lugadeen_US
dc.date.accessioned2020-10-14T08:26:09Z-
dc.date.available2020-10-14T08:26:09Z-
dc.date.issued2020-04-01en_US
dc.identifier.issn21682208en_US
dc.identifier.issn21682194en_US
dc.identifier.other2-s2.0-85074302030en_US
dc.identifier.other10.1109/JBHI.2019.2930091en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85074302030&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/70246-
dc.description.abstract© 2013 IEEE. As turns and walking speed modulation are crucial for functional mobility, development of a field-based tool to objectively evaluate non-steady-state gait is essential. This study aimed to quantify spatiotemporal gait using three Android smartphones during steady-state walking, turns, and gait speed modulation in laboratory and free-living environments. In total, 24 adults ambulated along a 10-m walkway in both environments under seven conditions: straight walking, 90° left or right turn, and modulating gait speed from usual-slow, usual-fast, slow-fast, and fast-slow. Two smartphones were attached to the body, with another phone placed in a shoulder bag. Gait velocity, step time, step length, cadence, and symmetry were computed from smartphone-based tri-axial accelerometers and validated with motion capture and video, in laboratory and free-living environments, respectively. Validity was assessed using Pearson's correlation and Bland-Altman analysis. Gait velocity results revealed moderate to very high validity across all walking conditions, smartphone models, smartphone locations, and environments. Correlations for gait velocity ranged between 0.87-0.91 and 0.79-0.83 for straight walking, 0.86-0.95 and 0.86-0.89 for turning, and 0.51-0.90 and 0.67-0.89 for speed modulation trials, in laboratory and free-living environments, respectively. Step time, step length, and cadence demonstrated high to very high correlations for straight walking and turns. However, symmetry results revealed high correlations only during straight walking in the laboratory. Conditions that included slow walking showed negligible to moderate validity with a high bias. In conclusion, smartphones can be employed as field-based devices to assess steady-state walking, turning, and speed modulation across environment, model, and placement when walking faster than 0.5 m/s.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.subjectHealth Professionsen_US
dc.titleSmartphone-Based Assessment of Gait during Straight Walking, Turning, and Walking Speed Modulation in Laboratory and Free-Living Environmentsen_US
dc.typeJournalen_US
article.title.sourcetitleIEEE Journal of Biomedical and Health Informaticsen_US
article.volume24en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsControl One LLCen_US
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