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Human gesture classification by brute-force machine learning for exergaming in physiotherapy
- Source :
- CIG, 2016 IEEE Conference on Computational Intelligence and Games (CIG)
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods.
- Subjects :
- random forests
Computer science
Speech recognition
human gesture classification
02 engineering and technology
computer.software_genre
support vector machines
online continuous human gesture recognition
Radio frequency
0202 electrical engineering, electronic engineering, information engineering
self-captured stealth game gesture dataset
Contextual image classification
Video game development
exergaming
Random forest
random processes
Microsoft Research Cambridge-12 Kinect dataset
computer games
020201 artificial intelligence & image processing
automatic gesture recognition
Games
dynamic gesture recognition
Gesture
medicine.medical_specialty
Technology and Engineering
Decision trees
Decision tree
patient treatment
Machine learning
leave-one-subject-out cross-validation
Gesture recognition
medicine
Training
temporal dimension
physiotherapy
Skeleton
brute-force machine learning
Vegetation
business.industry
020207 software engineering
brute-force classification strategy
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
adaptive game development
Physical therapy
learning (artificial intelligence)
Artificial intelligence
business
Classifier (UML)
computer
image classification
Subjects
Details
- ISBN :
- 978-1-5090-1883-3
- ISSN :
- 23254289
- ISBNs :
- 9781509018833
- Database :
- OpenAIRE
- Journal :
- 2016 IEEE Conference on Computational Intelligence and Games (CIG)
- Accession number :
- edsair.doi.dedup.....8f0e80bec5275673e59b94a42a20bc7a