Name
Abstract | ||
|---|---|---|
This paper analyses gait patterns of patients with Parkinson;s Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BSN.2015.7299396 | 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) |
Keywords | Field | DocType |
parkinson,s disease (PD),heel contact,gait,e-AR (ear-worn activity recognition) sensor,CODA | Computer vision,Coda,Parkinson's disease,Gait,Computer science,Effect of gait parameters on energetic cost,Speech recognition,Gait analysis,Artificial intelligence,Physical medicine and rehabilitation | Conference |
ISSN | Citations | PageRank |
2376-8886 | 1 | 0.36 |
References | Authors | |
5 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Delaram Jarchi | 1 | 66 | 10.96 |
| Amy Peters | 2 | 1 | 0.36 |
| Benny Lo | 3 | 403 | 37.89 |
| Eirini Kalliolia | 4 | 1 | 0.36 |
| Irene Di Giulio | 5 | 1 | 0.36 |
| Patricia Limousin | 6 | 36 | 3.98 |
| Brian L Day | 7 | 1 | 0.69 |
| Guang-Zhong Yang | 8 | 2812 | 297.66 |