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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/10.20385/1860-2037/11.2014.8
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Bibtex

@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"
}
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RIS

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  -
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Wordbib

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<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&apos;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&apos;s objectives while also improving their ability to perform reference gestures accurately.</b:Comments>
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ISI

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.
ER
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Mods

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  <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>
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Full Metadata

Bibliographic Citation JVRB, 11(2014), no. 8.
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
swd
  • 4147490-9, Cerebral palsy
  • 4045960-3, Physiotherapie
  • 4120906-0, Applikation <Software>
DDC 004
Rights DPPL
URN: urn:nbn:de:0009-6-40200
DOI https://doi.org/10.20385/1860-2037/11.2014.8