Activity Classification for Interactive Game Interfaces

We present a technique for modeling and recognising human activity from moving light displays using hidden Markov models. We extract a small number of joint angles at each frame to form a feature vector. Continuous hidden Markov models are then trained with the resulting time series, one for each of...

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Main Authors: John Darby, Baihua Li, Nick Costen
Format: Article
Language:English
Published: Wiley 2008-01-01
Series:International Journal of Computer Games Technology
Online Access:http://dx.doi.org/10.1155/2008/751268
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author John Darby
Baihua Li
Nick Costen
author_facet John Darby
Baihua Li
Nick Costen
author_sort John Darby
collection DOAJ
description We present a technique for modeling and recognising human activity from moving light displays using hidden Markov models. We extract a small number of joint angles at each frame to form a feature vector. Continuous hidden Markov models are then trained with the resulting time series, one for each of a variety of human activity, using the Baum-Welch algorithm. Motion classification is then attempted by evaluation of the forward variable for each model using previously unseen test data. Experimental results based on real-world human motion capture data demonstrate the performance of the algorithm and some degree of robustness to data noise and human motion irregularity. This technique has potential applications in activity classification for gesture-based game interfaces and character animation.
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series International Journal of Computer Games Technology
spelling doaj-art-67b4e0adea1c4ed59a69f5d98cc3b16d2025-08-20T02:35:24ZengWileyInternational Journal of Computer Games Technology1687-70471687-70552008-01-01200810.1155/2008/751268751268Activity Classification for Interactive Game InterfacesJohn Darby0Baihua Li1Nick Costen2Department of Computing and Mathematics, The Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, UKDepartment of Computing and Mathematics, The Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, UKDepartment of Computing and Mathematics, The Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester M1 5GD, UKWe present a technique for modeling and recognising human activity from moving light displays using hidden Markov models. We extract a small number of joint angles at each frame to form a feature vector. Continuous hidden Markov models are then trained with the resulting time series, one for each of a variety of human activity, using the Baum-Welch algorithm. Motion classification is then attempted by evaluation of the forward variable for each model using previously unseen test data. Experimental results based on real-world human motion capture data demonstrate the performance of the algorithm and some degree of robustness to data noise and human motion irregularity. This technique has potential applications in activity classification for gesture-based game interfaces and character animation.http://dx.doi.org/10.1155/2008/751268
spellingShingle John Darby
Baihua Li
Nick Costen
Activity Classification for Interactive Game Interfaces
International Journal of Computer Games Technology
title Activity Classification for Interactive Game Interfaces
title_full Activity Classification for Interactive Game Interfaces
title_fullStr Activity Classification for Interactive Game Interfaces
title_full_unstemmed Activity Classification for Interactive Game Interfaces
title_short Activity Classification for Interactive Game Interfaces
title_sort activity classification for interactive game interfaces
url http://dx.doi.org/10.1155/2008/751268
work_keys_str_mv AT johndarby activityclassificationforinteractivegameinterfaces
AT baihuali activityclassificationforinteractivegameinterfaces
AT nickcosten activityclassificationforinteractivegameinterfaces