Ensemble Classifier-Based Physical Disorder Recognition System Using Kinect Sensor View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Sriparna Saha , Monalisa Pal , Amit Konar , Jayahsree Roy

ABSTRACT

Gestures symbolizing body pain is a major challenging area in today’s world. We have presented a system here using Microsoft’s Kinect sensor for physical disorder recognition. Kinect sensor using its proprietary software development kit (SDK) approximates the human body in terms of 20 joint coordinates in three-dimensional space. We have taken into account 24 gestures related to physical disorder. For the training part, 3 datasets are formed where each dataset is constructed with the data acquired from 20 different subjects. For real time implementation, Kinect sensor is employed in laboratory for 24 h monitoring. The datasets are divided into 4:1 ratio for training and testing, respectively. After determining 171 features from each frame, ensemble classifier (in bagging framework) performs disorder recognition and yields 94.65 % accuracy in 0.0516 s. More... »

PAGES

169-175

Book

TITLE

Computational Advancement in Communication Circuits and Systems

ISBN

978-81-322-2273-6
978-81-322-2274-3

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-81-322-2274-3_21

DOI

http://dx.doi.org/10.1007/978-81-322-2274-3_21

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1021900215


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