Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation
Identifieur interne : 000621 ( Main/Exploration ); précédent : 000620; suivant : 000622Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation
Auteurs : Nicole A. Capela [Canada] ; Edward D. Lemaire [Canada] ; Natalie Baddour [Canada]Source :
- Journal of NeuroEngineering and Rehabilitation [ 1743-0003 ] ; 2015.
Abstract
The 6-minute walk test (6MWT: the maximum distance walked in 6 minutes) is used by rehabilitation professionals as a measure of exercise capacity. Today’s smartphones contain hardware that can be used for wearable sensor applications and mobile data analysis. A smartphone application can run the 6MWT and provide typically unavailable biomechanical information about how the person moves during the test.
A new algorithm for a calibration-free 6MWT smartphone application was developed that uses the test’s inherent conditions and smartphone accelerometer-gyroscope data to report the total distance walked, step timing, gait symmetry, and walking changes over time. This information is not available with a standard 6MWT and could help with clinical decision-making.
The 6MWT application was evaluated with 15 able-bodied participants. A BlackBerry Z10 smartphone was worn on a belt at the mid lower back. Audio from the phone instructed the person to start and stop walking. Digital video was independently recorded during the trial as a gold-standard comparator.
The average difference between smartphone and gold standard foot strike timing was 0.014 ± 0.015 s. The total distance calculated by the application was within 1 m of the measured distance for all but one participant, which was more accurate than other smartphone-based studies.
These results demonstrated that clinically relevant 6MWT results can be achieved with typical smartphone hardware and a novel algorithm.
The online version of this article (doi:10.1186/s12984-015-0013-9) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12984-015-0013-9
PubMed: 25889112
PubMed Central: 4343050
Affiliations:
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Capela</name><affiliation wicri:level="1"><nlm:aff id="Aff1">Ottawa Hospital Research Institute, Ottawa, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Ottawa Hospital Research Institute, Ottawa</wicri:regionArea><wicri:noRegion>Ottawa</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff3">Mechanical Engineering, University of Ottawa, Ottawa, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Mechanical Engineering, University of Ottawa, Ottawa</wicri:regionArea><wicri:noRegion>Ottawa</wicri:noRegion></affiliation></author><author><name sortKey="Lemaire, Edward D" sort="Lemaire, Edward D" uniqKey="Lemaire E" first="Edward D" last="Lemaire">Edward D. 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Capela</name><affiliation wicri:level="1"><nlm:aff id="Aff1">Ottawa Hospital Research Institute, Ottawa, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Ottawa Hospital Research Institute, Ottawa</wicri:regionArea><wicri:noRegion>Ottawa</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff3">Mechanical Engineering, University of Ottawa, Ottawa, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Mechanical Engineering, University of Ottawa, Ottawa</wicri:regionArea><wicri:noRegion>Ottawa</wicri:noRegion></affiliation></author><author><name sortKey="Lemaire, Edward D" sort="Lemaire, Edward D" uniqKey="Lemaire E" first="Edward D" last="Lemaire">Edward D. Lemaire</name><affiliation wicri:level="1"><nlm:aff id="Aff1">Ottawa Hospital Research Institute, Ottawa, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Ottawa Hospital Research Institute, Ottawa</wicri:regionArea><wicri:noRegion>Ottawa</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:aff id="Aff2">Faculty of Medicine, University of Ottawa, Ottawa, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Faculty of Medicine, University of Ottawa, Ottawa</wicri:regionArea><wicri:noRegion>Ottawa</wicri:noRegion></affiliation></author><author><name sortKey="Baddour, Natalie" sort="Baddour, Natalie" uniqKey="Baddour N" first="Natalie" last="Baddour">Natalie Baddour</name><affiliation wicri:level="1"><nlm:aff id="Aff3">Mechanical Engineering, University of Ottawa, Ottawa, Canada</nlm:aff><country xml:lang="fr">Canada</country><wicri:regionArea>Mechanical Engineering, University of Ottawa, Ottawa</wicri:regionArea><wicri:noRegion>Ottawa</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of NeuroEngineering and Rehabilitation</title><idno type="eISSN">1743-0003</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>The 6-minute walk test (6MWT: the maximum distance walked in 6 minutes) is used by rehabilitation professionals as a measure of exercise capacity. Today’s smartphones contain hardware that can be used for wearable sensor applications and mobile data analysis. A smartphone application can run the 6MWT and provide typically unavailable biomechanical information about how the person moves during the test.</p></sec><sec><title>Methods</title><p>A new algorithm for a calibration-free 6MWT smartphone application was developed that uses the test’s inherent conditions and smartphone accelerometer-gyroscope data to report the total distance walked, step timing, gait symmetry, and walking changes over time. This information is not available with a standard 6MWT and could help with clinical decision-making.</p><p>The 6MWT application was evaluated with 15 able-bodied participants. A BlackBerry Z10 smartphone was worn on a belt at the mid lower back. Audio from the phone instructed the person to start and stop walking. Digital video was independently recorded during the trial as a gold-standard comparator.</p></sec><sec><title>Results</title><p>The average difference between smartphone and gold standard foot strike timing was 0.014 ± 0.015 s. The total distance calculated by the application was within 1 m of the measured distance for all but one participant, which was more accurate than other smartphone-based studies.</p></sec><sec><title>Conclusions</title><p>These results demonstrated that clinically relevant 6MWT results can be achieved with typical smartphone hardware and a novel algorithm.</p></sec><sec><title>Electronic supplementary material</title><p>The online version of this article (doi:10.1186/s12984-015-0013-9) contains supplementary material, which is available to authorized users.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Senden, R" uniqKey="Senden R">R Senden</name></author><author><name sortKey="Grimm, B" uniqKey="Grimm B">B Grimm</name></author><author><name sortKey="Heyligers, Ic" uniqKey="Heyligers I">IC Heyligers</name></author><author><name sortKey="Savelberg, Hhcm" uniqKey="Savelberg H">HHCM Savelberg</name></author><author><name 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