NDLI: A smartphone approach for the 2 and 6-minute walk test

Content Provider	IEEE Xplore Digital Library
Author	Capela, N.A. Lemaire, E.D. Baddour, N.C.
Copyright Year	2014
Description	Author affiliation: Ottawa Hosp. Res. Inst., Ottawa, ON, Canada (Capela, N.A.) \|\| Dept. of Mech. Eng., Univ. of Ottawa, Ottawa, ON, Canada (Baddour, N.C.) \|\| Ottawa Hosp. Res. Inst., Univ. of Ottawa, Ottawa, ON, Canada (Lemaire, E.D.)
Abstract	The 2 and 6-minute walk tests (2-6MWT) are used by rehabilitation professionals as a measure of exercise capacity. Our research has produced a new 2-6MWT BlackBerry smartphone application (app) that can be used to run the 2-6MWT and also provide new information about how the person moves during the test. The smartphone is worn on a belt at the lower back to record phone sensor data while walking. This data is used to identify foot strikes, calculate the total distance walked and step timing, and analyze pelvis accelerations. Information on symmetry, walking changes over time, and poor walking patterns is not available from a typical 2-6MWT and could help with clinical decision-making. The 2-6MWT app was evaluated in a pilot test using data from five able-bodied participants. Foot strike time was within 0.07 seconds when compared to gold standard video recordings. The total distance calculated by the app was within 1m of the measured distance.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	958
Ending Page	961
File Size	799103
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6943751
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acceleration Accelerometers Foot Legged locomotion Atmospheric measurements Particle measurements Biomedical monitoring rehabilitation walking distance estimation pattern classification wearable monitoring
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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