| dc.creator | Cifuentes, Jenny | |
| dc.creator | Pham, Minh Tu | |
| dc.creator | Moreau, Richard | |
| dc.creator | Boulanger, Pierre | |
| dc.creator | Prieto, Flavio | |
| dc.date | 2019-07-01T07:00:00Z | |
| dc.date.accessioned | 2022-10-13T13:37:17Z | |
| dc.date.available | 2022-10-13T13:37:17Z | |
| dc.identifier | https://ciencia.lasalle.edu.co/scopus_unisalle/144 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4157972 | |
| dc.description | 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. | |
| dc.source | Biomedical Signal Processing and Control | |
| dc.source | 162 | |
| dc.subject | Curvature analysis | |
| dc.subject | Dynamic arc length warping | |
| dc.subject | Gesture classification | |
| dc.subject | Hand motion tracking | |
| dc.title | Medical gesture recognition using dynamic arc length warping | |
| dc.type | Article | |
