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Paper information

Medical gesture recognition using dynamic arc length warping

J. Cifuentes, M.T. Pham, R. Moreau, P. Boulanger, F. Prieto

Biomedical Signal Processing and Control Vol. 52, pp. 162 - 170

Summary:

Hand gesture recognition is a promising research area often used in applications of human–computer interactions in the medical field. In this paper, we present a novel approach to differentiate gestures based on an arc-length parametrization and a curvature analysis of 3D trajectories. This new method called dynamic arc length warping (DALW) can outperform classic multi dimensional-dynamic time warping (MD-DTW) algorithm as it is invariant to sensor location and more tolerant to temporal distortions. Experimental validation of the algorithm is presented using different gestures and sensors in biomedical applications: an exoskeleton apparatus, surgical gestures captured by an instrumented laparoscopic device and finally, a birth simulator with an instrumented forceps. A basic perceptron multilayer neural network was implemented in order to perform the classification. Results involve an average increase of 7.14% in the classification rates by using DALW distance, compared to the classical MD-DTW.


Keywords: Gesture classification; Curvature analysis; Dynamic arc length warping; Hand motion tracking


JCR Impact Factor and WoS quartile: 3.137 - Q2 (2019); 3.880 - Q2 (2020)

DOI reference: DOI icon 10.1016/j.bspc.2019.04.022

Published on paper: July 2019.

Published on-line: April 2019.



Citation:
J. Cifuentes, M.T. Pham, R. Moreau, P. Boulanger, F. Prieto. Medical gesture recognition using dynamic arc length warping. Biomedical Signal Processing and Control. Vol. 52, pp. 162 - 170, July 2019. [Online: April 2019]


    Research topics:
  • Mathematical Models and Artificial Intelligence in Healthcare

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