We will demonstrate a prototype exergame aimed at the serious domain of elderly fitness. The exergame incorporates straightforward means to gesture recognition, and utilises a Kinect camera to obtain 2.5D sensory data of the human user

Bakkes, S.

Cicek, E.

Gacem, B.

Kröse, B.

van Oosterhout, T.

Verbiest, H.

Vergouw, R.

We will demonstrate a prototype exergame aimed at the serious domain of elderly fitness. The exergame incorporates straightforward means to gesture recognition, and utilises a Kinect camera to obtain 2.5D sensory data of the human user.<br/

International Migration, Integration and Social Cohesion online publications

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Gesture recognition for an exergame prototypeGacem, B.; Vergouw, R.; Verbiest, H.; Cicek, E.; van Oosterhout, T.; Bakkes, S.; Kröse, B.Publication date2011Document VersionFinal published versionPublished inProceedings of the 23rd Benelux Conference on Artificial IntelligenceLink to publicationCitation for published version (APA):Gacem, B., Vergouw, R., Verbiest, H., Cicek, E., van Oosterhout, T., Bakkes, S., & Kröse, B.(2011). Gesture recognition for an exergame prototype. In P. De Causmaecker, J. Maervoet,T. Messelis, K. Verbeeck, & T. Vermeulen (Eds.), Proceedings of the 23rd BeneluxConference on Artificial Intelligence: 3-4 November 2011, Ghent, Belgium (pp. 457-458).(BNAIC; Vol. 23). BNAIC.http://allserv.kahosl.be/bnaic2011/sites/default/files/bnaic2011_submission_110.pdfGeneral rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:14 Apr 2023Gesture Recognition for an Exergame PrototypeBrahim Gacem Robert Vergouw Harm Verbiest Emrullah CicekTim van Oosterhout Sander Bakkes Ben KröseAmsterdam University of Applied Sciences (HvA), CREATE-IT Applied ResearchP.O. Box 1025, NL-1000 BA Amsterdam, The Netherlands{t.j.m.van.oosterhout, s.c.j.bakkes, b.j.a.krose}@hva.nlAbstractWe will demonstrate a prototype exergame aimed at the serious domain of elderly fitness. The exergameincorporates straightforward means to gesture recognition, and utilises a Kinect camera to obtain 2.5Dsensory data of the human user.1 IntroductionExergaming is a game genre that refers to utilising games and game principles for evoking a form of exercise[3]. As such, exergaming relies on technology to directly monitor the human player, typically by means oftracking body movement or reaction speed. The genre has been credited with upending the stereotype ofgaming as a sedentary activity, and promoting an active lifestyle [4, 1]. Exergames are seen as evolvingfrom technology changes aimed at making video games more fun [2].Students of the Amsterdam University of Applied Sciences (HvA) have investigated an exergame pro-totype that incorporates a straightforward form of gesture recognition. The research has been performed incollaboration with industry partner DIGIFiT, which develops new wellness applications in the field of fitnessand lifestyle, and applies smart technology to motivate people to live healthier and more active lifestyles.The intuition in this regard is that exergames may enable (elderly) people to monitor and effect theirhealth and wellbeing. Particularly, exergames fit in the public health vision to encourage elderly people toremain independently from care for as long as possible, by providing them with the tools for monitoring andeffecting their own health, and hence, maintaining more control on the care received.2 Exergame prototypeThe Ministry of Health, Welfare and Sport (Netherlands) supports the project Online Sports Club for theElderly. Part of this project is to utilise a 2.5D/time-of-flight camera in combination with a prototype ex-ergame. For the protoype, the gaming system needs to (1) receive input from a 2.5D camera by means of(third-party) sensory middleware, (2) determine the pose of the human player, and (3) subsequently visualisethe pose in an integrated 3D authoring tool.Of particular interest to an exergame built on gesture recognition is investigating how a gesture can bestbe determined:1. Trigger based. Is a particular gesture executed?2. Quality based. Is a particular gesture executed correctly?In this regard, the prototype incorporates measures for defining gestures such that they can be determinedboth absolutely as well as qualitatively. Technically, the prototype uses a 3D authoring tool to providefeedback to the user and game scripting (Unity3D), middleware for coarse interpretation of the sensor data(OpenNI), and a 2.5D camera (Microsoft Kinect). Connection between OpenNI en Unity3D is handled via awrapper that is maintained by OpenNI, and which is feature complete with regard to skeleton tracking (jointFigure 1: Screenshot of the exergame prototype. In the screenshot, the human player has to consecutivelytouch a series of in-game objects.positions and orientation). Calibration of the skeleton tracking takes place via a so-callled ‘psi’ position; aposition which the user has to adopt initially in order to allow the middleware to calibrate itself.1In the gaming prototype, gestures can straightforwardly be defined as a series of game objects with whicha collision can occur. Each game object is labelled with an order number, so the game script can determinewhether the objects are touched in the correct order, and, particularly, how tolerant the game should be whenfor instance one out of n objects has been accidentally skipped. In addition, audio samples are added toindividual game objects to provide feedback to the user. An example of a series of spheric game objects thathave to be touched in series, is illustrated in Figure 1. By way of exercise, the exergame prototype presentsusers with increasingly more challenging gestures upon each successfully completed gesture. The score isdetermined by weighting how many gestures have been executed, versus how correctly the gestures havebeen executed.We will demonstrate how gestures can be defined intuitively in the prototype, and how they can beutilised in a standard computer system to provide a straightforward form of gesture recognition based on2.5D sensor data. We will note that calibration to the user is presently relatively user unfriendly, though willbe improved in the next generation of the OpenNI middleware. Finally, we will explain directions for futureresearch, namely how geometric angles in join orientation can be applied to accurately define fine-grainedmotor skills.Acknowledgement. The research reported in this paper was supported by the SIA project ‘Mens voorde Lens’ and the SIA project ‘Smart Systems for Smart Services’.References[1] Nick Lewis. Exergaming ‘may combat kids’ sedentary lifestyles, 2009. Calgary Herald, 2009-06-19.[2] Tara Parker-Pope. The playstation workout: Videogames that get kids to jump, kick and sweat, 2005. Wall StreetJournal. October 4, 2005.[3] Jeff Sinclair, Philip Hingston, and Martin Masek. Considerations for the design of exergames. In Proceedings ofthe 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia,GRAPHITE ’07, pages 289–295, New York, NY, USA, 2007. ACM.[4] Amy van Aarem. ‘Exergaming’ helps jump-start sedentary children, 2008. The Boston Globe, 2008-01-10.1By the end of the year, OpenNI will release an update that makes it possible to initiate user skeleton tracking without requiring the‘psi’ calibation pose. The developer’s internal milestone for this functionality is September/October, with release shortly thereafter.

English

Gesture recognition for an exergame prototype

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Gesture recognition for an exergame prototypeGacem, B.; Vergouw, R.; Verbiest, H.; Cicek, E.; van Oosterhout, T.; Bakkes, S.; Kröse, B.Publication date2011Document VersionFinal published versionPublished inProceedings of the 23rd Benelux Conference on Artificial IntelligenceLink to publicationCitation for published version (APA):Gacem, B., Vergouw, R., Verbiest, H., Cicek, E., van Oosterhout, T., Bakkes, S., & Kröse, B.(2011). Gesture recognition for an exergame prototype. In P. De Causmaecker, J. Maervoet,T. Messelis, K. Verbeeck, & T. Vermeulen (Eds.), Proceedings of the 23rd BeneluxConference on Artificial Intelligence: 3-4 November 2011, Ghent, Belgium (pp. 457-458).(BNAIC; Vol. 23). BNAIC.http://allserv.kahosl.be/bnaic2011/sites/default/files/bnaic2011_submission_110.pdfGeneral rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:09 Mar 2023Gesture Recognition for an Exergame PrototypeBrahim Gacem Robert Vergouw Harm Verbiest Emrullah CicekTim van Oosterhout Sander Bakkes Ben KröseAmsterdam University of Applied Sciences (HvA), CREATE-IT Applied ResearchP.O. Box 1025, NL-1000 BA Amsterdam, The Netherlands{t.j.m.van.oosterhout, s.c.j.bakkes, b.j.a.krose}@hva.nlAbstractWe will demonstrate a prototype exergame aimed at the serious domain of elderly fitness. The exergameincorporates straightforward means to gesture recognition, and utilises a Kinect camera to obtain 2.5Dsensory data of the human user.1 IntroductionExergaming is a game genre that refers to utilising games and game principles for evoking a form of exercise[3]. As such, exergaming relies on technology to directly monitor the human player, typically by means oftracking body movement or reaction speed. The genre has been credited with upending the stereotype ofgaming as a sedentary activity, and promoting an active lifestyle [4, 1]. Exergames are seen as evolvingfrom technology changes aimed at making video games more fun [2].Students of the Amsterdam University of Applied Sciences (HvA) have investigated an exergame pro-totype that incorporates a straightforward form of gesture recognition. The research has been performed incollaboration with industry partner DIGIFiT, which develops new wellness applications in the field of fitnessand lifestyle, and applies smart technology to motivate people to live healthier and more active lifestyles.The intuition in this regard is that exergames may enable (elderly) people to monitor and effect theirhealth and wellbeing. Particularly, exergames fit in the public health vision to encourage elderly people toremain independently from care for as long as possible, by providing them with the tools for monitoring andeffecting their own health, and hence, maintaining more control on the care received.2 Exergame prototypeThe Ministry of Health, Welfare and Sport (Netherlands) supports the project Online Sports Club for theElderly. Part of this project is to utilise a 2.5D/time-of-flight camera in combination with a prototype ex-ergame. For the protoype, the gaming system needs to (1) receive input from a 2.5D camera by means of(third-party) sensory middleware, (2) determine the pose of the human player, and (3) subsequently visualisethe pose in an integrated 3D authoring tool.Of particular interest to an exergame built on gesture recognition is investigating how a gesture can bestbe determined:1. Trigger based. Is a particular gesture executed?2. Quality based. Is a particular gesture executed correctly?In this regard, the prototype incorporates measures for defining gestures such that they can be determinedboth absolutely as well as qualitatively. Technically, the prototype uses a 3D authoring tool to providefeedback to the user and game scripting (Unity3D), middleware for coarse interpretation of the sensor data(OpenNI), and a 2.5D camera (Microsoft Kinect). Connection between OpenNI en Unity3D is handled via awrapper that is maintained by OpenNI, and which is feature complete with regard to skeleton tracking (jointFigure 1: Screenshot of the exergame prototype. In the screenshot, the human player has to consecutivelytouch a series of in-game objects.positions and orientation). Calibration of the skeleton tracking takes place via a so-callled ‘psi’ position; aposition which the user has to adopt initially in order to allow the middleware to calibrate itself.1In the gaming prototype, gestures can straightforwardly be defined as a series of game objects with whicha collision can occur. Each game object is labelled with an order number, so the game script can determinewhether the objects are touched in the correct order, and, particularly, how tolerant the game should be whenfor instance one out of n objects has been accidentally skipped. In addition, audio samples are added toindividual game objects to provide feedback to the user. An example of a series of spheric game objects thathave to be touched in series, is illustrated in Figure 1. By way of exercise, the exergame prototype presentsusers with increasingly more challenging gestures upon each successfully completed gesture. The score isdetermined by weighting how many gestures have been executed, versus how correctly the gestures havebeen executed.We will demonstrate how gestures can be defined intuitively in the prototype, and how they can beutilised in a standard computer system to provide a straightforward form of gesture recognition based on2.5D sensor data. We will note that calibration to the user is presently relatively user unfriendly, though willbe improved in the next generation of the OpenNI middleware. Finally, we will explain directions for futureresearch, namely how geometric angles in join orientation can be applied to accurately define fine-grainedmotor skills.Acknowledgement. The research reported in this paper was supported by the SIA project ‘Mens voorde Lens’ and the SIA project ‘Smart Systems for Smart Services’.References[1] Nick Lewis. Exergaming ‘may combat kids’ sedentary lifestyles, 2009. Calgary Herald, 2009-06-19.[2] Tara Parker-Pope. The playstation workout: Videogames that get kids to jump, kick and sweat, 2005. Wall StreetJournal. October 4, 2005.[3] Jeff Sinclair, Philip Hingston, and Martin Masek. Considerations for the design of exergames. In Proceedings ofthe 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia,GRAPHITE ’07, pages 289–295, New York, NY, USA, 2007. ACM.[4] Amy van Aarem. ‘Exergaming’ helps jump-start sedentary children, 2008. The Boston Globe, 2008-01-10.1By the end of the year, OpenNI will release an update that makes it possible to initiate user skeleton tracking without requiring the‘psi’ calibation pose. The developer’s internal milestone for this functionality is September/October, with release shortly thereafter.

Gesture recognition for an exergame prototype

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