Ir arriba
Información del artículo

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

Resumen:

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.


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


Índice de impacto JCR y cuartil WoS: 3,137 - Q2 (2019); 4,900 - Q1 (2023)

Referencia DOI: DOI icon https://doi.org/10.1016/j.bspc.2019.04.022

Publicado en papel: Julio 2019.

Publicado on-line: Abril 2019.



Cita:
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, Julio 2019. [Online: Abril 2019]


    Líneas de investigación:
  • Modelos matemáticos e Inteligencia Artificial aplicados al sector de la salud

pdf Previsualizar
pdf Solicitar el artículo completo a los autores