Abstract
Detection of 14 healthcare-related gestures due to pain at different body parts is the target area of this work using Kinect sensor. The novelty of our work lies in suppressing the problem of compensation by the use of probability while using similarity matching technique for gesture recognition. The adopted method enhances the matching accuracy for all the similarity measures. A shared probability and similarity measure-based metric has been defined as the matching index. This unique technique contributes to field of health care under static gesture recognition as an application of machine learning with a high accuracy of 99.1071% in 0.0126 s using probability-induced city-block distance.
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Acknowledgements
The research work is supported by the University Grants Commission, India, University with Potential for Excellence Program (Phase II) in Cognitive Science, Jadavpur University and University Grants Commission (UGC) for providing fellowship to the second author.
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Pal, M., Saha, S., Konar, A. (2018). Probability-Induced Distance-Based Gesture Matching for Health care Using Microsoft’s Kinect Sensor. In: Sa, P., Sahoo, M., Murugappan, M., Wu, Y., Majhi, B. (eds) Progress in Intelligent Computing Techniques: Theory, Practice, and Applications. Advances in Intelligent Systems and Computing, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-10-3373-5_28
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DOI: https://doi.org/10.1007/978-981-10-3373-5_28
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