Name
Title | ||
|---|---|---|
Gait parameter estimation from a miniaturized ear-worn sensor using singular spectrum analysis and longest common subsequence. |
Abstract | ||
|---|---|---|
This paper presents a new approach to gait analysis and parameter estimation from a single miniaturized ear-worn sensor embedded with a triaxial accelerometer. Singular spectrum analysis combined with the longest common subsequence algorithm has been used as a basis for gait parameter estimation. It incorporates information from all axes of the accelerometer to estimate parameters including swing, stance, and stride times. Rather than only using local features of the raw signals, the periodicity of the signals is also taken into account. The hypotheses tested by this study include: 1) how accurate is the ear-worn sensor in terms of gait parameter extraction compared to the use of an instrumented treadmill; 2) does the ear-worn sensor provide a feasible option for assessment and quantification of gait pattern changes. Key gait events for normal subjects such as heel contact and toe off are validated with a high-speed camera, as well as a force-plate instrumented treadmill. Ten healthy adults walked for 20 min on a treadmill with an increasing incline of 2% every 2 min. The upper and lower limits of the absolute errors using 95% confidence intervals for swing, stance, and stride times were obtained as 35.5 ±3.99 ms, 36.9 ±3.84 ms, and 17.9 ±2.29 ms, respectively. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TBME.2014.2299772 | IEEE Trans. Biomed. Engineering |
Keywords | Field | DocType |
accelerometers,biomedical equipment,ear,gait analysis,geriatrics,medical signal processing,force-plate instrumented treadmill,gait analysis,gait parameter estimation,gait parameter extraction,gait pattern changes,healthy adults,heel contact,high-speed camera,miniaturized ear-worn sensor,raw signals,signal periodicity,singular spectrum analysis,stance,stride times,swing,time 20 min,toe off,triaxial accelerometer,walking,Body sensor networks,e-AR sensor,gait,longest common subsequence (LCSS),singular spectrum analysis (SSA) | Computer vision,Longest common subsequence problem,STRIDE,Gait,Accelerometer,Computer science,Effect of gait parameters on energetic cost,Electronic engineering,Gait analysis,Artificial intelligence,Treadmill,Swing | Journal |
Volume | Issue | ISSN |
61 | 4 | 1558-2531 |
Citations | PageRank | References |
8 | 0.69 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Delaram Jarchi | 1 | 66 | 10.96 |
| Charence Wong | 2 | 152 | 7.33 |
| Richard Mark Kwasnicki | 3 | 8 | 1.03 |
| Ben Heller | 4 | 19 | 3.32 |
| Garry A Tew | 5 | 8 | 1.03 |
| Guang-Zhong Yang | 6 | 2812 | 297.66 |