Name
Abstract | ||
|---|---|---|
We describe a new gait segmentation method based on the continuous wavelet transform to identify stride-by-stride gait cycles from measurements of foot-mounted three-dimensional (3D) accelerometers. The detection of such gait cycles is indeed a crucial step for an accurate extraction of relevant gait events such as heel strike, toe strike, heel-off, and toe-off. We demonstrate the ability of this segmentation method, used in conjunction with a validated extraction algorithm, to calculate the following gait (duration) parameters for each gait cycle during the gait of a healthy young subject and of an elderly subject with Parkinson's disease (PD) in OFF and ON states: durations of (1) loading response, (2) mid-stance, (3) push-off, (4) stance, (5) swing, (6) stride, (7) step, and (8) double support phases. The experimental results show that the proposed method can extract relevant refined gait parameters to quantify subtle gait disturbances in subjects with PD. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IC3D.2015.7391836 | 2015 International Conference on 3D Imaging (IC3D) |
Keywords | Field | DocType |
3D,segmentation of gait cycles,biomedical signal processing,gait analysis,accelerometers,Parkinson's disease | Computer vision,Gait,STRIDE,Accelerometer,Computer science,Effect of gait parameters on energetic cost,Continuous wavelet transform,Heel,Gait analysis,Artificial intelligence,Gait Disturbance | Conference |
ISSN | Citations | PageRank |
2379-1772 | 1 | 0.41 |
References | Authors | |
8 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Boutaayamou | 1 | 8 | 2.40 |
| Olivier Bruls | 2 | 1 | 0.41 |
| Vincent Denoel | 3 | 1 | 0.41 |
| Cédric Schwartz | 4 | 5 | 2.27 |
| Marie Demonceau | 5 | 8 | 2.06 |
| Gaëtan Garraux | 6 | 21 | 3.21 |
| Jacques G. Verly | 7 | 77 | 14.48 |