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Computing and Visualization in Science

, Volume 22, Issue 1–4, pp 1–14 | Cite as

Triangular membership function based real-time gesture monitoring system for physical disorder detection

  • Sriparna Saha
  • Monalisa PalEmail author
  • Amit Konar
S.I.: ICACNI 2016
  • 135 Downloads

Abstract

A novel approach to distinguish 25 body gestures enlightening physical disorders in young and elder individuals is explained using the proposed system. Here a well-known human sensing device, Kinect sensor is used which approximates the human body by virtue of 20 body joints and produces a data stream from which skeleton of the human body is traced. Sampling rate of the data stream is 30 frames per second where every frame represents a body gesture. The overall system is bifurcated into two parts. The offline part calculates 19 features from each frame representing a diseased gesture. These features are angle and distance information between 20 body joints. Features correspond to a definite pattern for a specific body gesture. In online part, triangular fuzzy matching based algorithm performs to detect real-time gestures with 90.57% accuracy. For achieving better accuracy, decision tree is enforced to separate sitting and standing body gestures. The proposed approach is observed to outperform several contemporary approaches in terms of accuracy while presenting a simple system which is based on medical knowledge and is capable of distinguishing as large as 25 gestures.

Keywords

Decision tree Kinect sensor Fuzzy membership function Physical disorder Physiotherapy 

Notes

Acknowledgements

We are thankful to the doctors of Calcutta Medical College and Hospital, specially Dr. Subhasish Saha, Head and Professor of Orthopedics Department, for his kind and generous support for preparation of the datasets. We are thankful to the organising committee of 4th International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2016) and of Computing and Visualization in Science journal for giving us this opportunity to present the extension of our work in the special issue of this journal.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMaulana Abul Kalam Azad University of TechnologyKolkataIndia
  2. 2.Artificial Intelligence Lab., Electronics and Telecommunication Engineering DepartmentJadavpur UniversityKolkataIndia

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