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Christian Barrett, Jacob Brown, Jay Hartford, Michael Hoerter, Andrew Kennedy, Ray Hassan, and David Whittinghill, Estimating Gesture Accuracy in Motion-Based Health Games. Journal of Virtual Reality and Broadcasting, 11(2014), no. 8. (urn:nbn:de:0009-6-40200)
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%0 Journal Article %T Estimating Gesture Accuracy in Motion-Based Health Games %A Barrett, Christian %A Brown, Jacob %A Hartford, Jay %A Hoerter, Michael %A Kennedy, Andrew %A Hassan, Ray %A Whittinghill, David %J Journal of Virtual Reality and Broadcasting %D 2014 %V 11(2014) %N 8 %@ 1860-2037 %F barrett2014 %X This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player's gestures, converting them to a three-dimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game's objectives while also improving their ability to perform reference gestures accurately. %L 004 %K KINECT %K RGB-D camera %K algorithms %K application development %K cerebral palsy %K health games %K physical therapy %K serious games %R 10.20385/1860-2037/11.2014.8 %U http://nbn-resolving.de/urn:nbn:de:0009-6-40200 %U http://dx.doi.org/10.20385/1860-2037/11.2014.8Download
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@Article{barrett2014, author = "Barrett, Christian and Brown, Jacob and Hartford, Jay and Hoerter, Michael and Kennedy, Andrew and Hassan, Ray and Whittinghill, David", title = "Estimating Gesture Accuracy in Motion-Based Health Games", journal = "Journal of Virtual Reality and Broadcasting", year = "2014", volume = "11(2014)", number = "8", keywords = "KINECT; RGB-D camera; algorithms; application development; cerebral palsy; health games; physical therapy; serious games", abstract = "This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player's gestures, converting them to a three-dimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game's objectives while also improving their ability to perform reference gestures accurately.", issn = "1860-2037", doi = "10.20385/1860-2037/11.2014.8", url = "http://nbn-resolving.de/urn:nbn:de:0009-6-40200" }Download
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TY - JOUR AU - Barrett, Christian AU - Brown, Jacob AU - Hartford, Jay AU - Hoerter, Michael AU - Kennedy, Andrew AU - Hassan, Ray AU - Whittinghill, David PY - 2014 DA - 2014// TI - Estimating Gesture Accuracy in Motion-Based Health Games JO - Journal of Virtual Reality and Broadcasting VL - 11(2014) IS - 8 KW - KINECT KW - RGB-D camera KW - algorithms KW - application development KW - cerebral palsy KW - health games KW - physical therapy KW - serious games AB - This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player's gestures, converting them to a three-dimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game's objectives while also improving their ability to perform reference gestures accurately. SN - 1860-2037 UR - http://nbn-resolving.de/urn:nbn:de:0009-6-40200 DO - 10.20385/1860-2037/11.2014.8 ID - barrett2014 ER -Download
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<?xml version="1.0" encoding="UTF-8"?> <b:Sources SelectedStyle="" xmlns:b="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" > <b:Source> <b:Tag>barrett2014</b:Tag> <b:SourceType>ArticleInAPeriodical</b:SourceType> <b:Year>2014</b:Year> <b:PeriodicalTitle>Journal of Virtual Reality and Broadcasting</b:PeriodicalTitle> <b:Volume>11(2014)</b:Volume> <b:Issue>8</b:Issue> <b:Url>http://nbn-resolving.de/urn:nbn:de:0009-6-40200</b:Url> <b:Url>http://dx.doi.org/10.20385/1860-2037/11.2014.8</b:Url> <b:Author> <b:Author><b:NameList> <b:Person><b:Last>Barrett</b:Last><b:First>Christian</b:First></b:Person> <b:Person><b:Last>Brown</b:Last><b:First>Jacob</b:First></b:Person> <b:Person><b:Last>Hartford</b:Last><b:First>Jay</b:First></b:Person> <b:Person><b:Last>Hoerter</b:Last><b:First>Michael</b:First></b:Person> <b:Person><b:Last>Kennedy</b:Last><b:First>Andrew</b:First></b:Person> <b:Person><b:Last>Hassan</b:Last><b:First>Ray</b:First></b:Person> <b:Person><b:Last>Whittinghill</b:Last><b:First>David</b:First></b:Person> </b:NameList></b:Author> </b:Author> <b:Title>Estimating Gesture Accuracy in Motion-Based Health Games</b:Title> <b:Comments>This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player's gestures, converting them to a three-dimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game's objectives while also improving their ability to perform reference gestures accurately.</b:Comments> </b:Source> </b:Sources>Download
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PT Journal AU Barrett, C Brown, J Hartford, J Hoerter, M Kennedy, A Hassan, R Whittinghill, D TI Estimating Gesture Accuracy in Motion-Based Health Games SO Journal of Virtual Reality and Broadcasting PY 2014 VL 11(2014) IS 8 DI 10.20385/1860-2037/11.2014.8 DE KINECT; RGB-D camera; algorithms; application development; cerebral palsy; health games; physical therapy; serious games AB This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player's gestures, converting them to a three-dimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game's objectives while also improving their ability to perform reference gestures accurately. ERDownload
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<mods> <titleInfo> <title>Estimating Gesture Accuracy in Motion-Based Health Games</title> </titleInfo> <name type="personal"> <namePart type="family">Barrett</namePart> <namePart type="given">Christian</namePart> </name> <name type="personal"> <namePart type="family">Brown</namePart> <namePart type="given">Jacob</namePart> </name> <name type="personal"> <namePart type="family">Hartford</namePart> <namePart type="given">Jay</namePart> </name> <name type="personal"> <namePart type="family">Hoerter</namePart> <namePart type="given">Michael</namePart> </name> <name type="personal"> <namePart type="family">Kennedy</namePart> <namePart type="given">Andrew</namePart> </name> <name type="personal"> <namePart type="family">Hassan</namePart> <namePart type="given">Ray</namePart> </name> <name type="personal"> <namePart type="family">Whittinghill</namePart> <namePart type="given">David</namePart> </name> <abstract>This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player's gestures, converting them to a three-dimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game's objectives while also improving their ability to perform reference gestures accurately.</abstract> <subject> <topic>KINECT</topic> <topic>RGB-D camera</topic> <topic>algorithms</topic> <topic>application development</topic> <topic>cerebral palsy</topic> <topic>health games</topic> <topic>physical therapy</topic> <topic>serious games</topic> </subject> <classification authority="ddc">004</classification> <relatedItem type="host"> <genre authority="marcgt">periodical</genre> <genre>academic journal</genre> <titleInfo> <title>Journal of Virtual Reality and Broadcasting</title> </titleInfo> <part> <detail type="volume"> <number>11(2014)</number> </detail> <detail type="issue"> <number>8</number> </detail> <date>2014</date> </part> </relatedItem> <identifier type="issn">1860-2037</identifier> <identifier type="urn">urn:nbn:de:0009-6-40200</identifier> <identifier type="doi">10.20385/1860-2037/11.2014.8</identifier> <identifier type="uri">http://nbn-resolving.de/urn:nbn:de:0009-6-40200</identifier> <identifier type="citekey">barrett2014</identifier> </mods>Download
Full Metadata
Bibliographic Citation | JVRB, 11(2014), no. 8. |
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Title |
Estimating Gesture Accuracy in Motion-Based Health Games (eng) |
Author | Christian Barrett, Jacob Brown, Jay Hartford, Michael Hoerter, Andrew Kennedy, Ray Hassan, David Whittinghill |
Language | eng |
Abstract | This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player's gestures, converting them to a three-dimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game's objectives while also improving their ability to perform reference gestures accurately. |
Subject | KINECT, RGB-D camera, algorithms, application development, cerebral palsy, health games, physical therapy, serious games |
Classified Subjects |
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DDC | 004 |
Rights | DPPL |
URN: | urn:nbn:de:0009-6-40200 |
DOI | https://doi.org/10.20385/1860-2037/11.2014.8 |