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Gait Parameter Estimation From a Miniaturized Ear-Worn Sensor Using Singular Spectrum Analysis and Longest Common Subsequence

Jarchi, Delaram, Wong, Charence, Kwasnicki, Richard Mark, Heller, Ben, Tew, Garry A., Yang, Guang-Zhong (2014) Gait Parameter Estimation From a Miniaturized Ear-Worn Sensor Using Singular Spectrum Analysis and Longest Common Subsequence. IEEE Transactions on Biomedical Engineering, 61 (4). pp. 1261-1273. ISSN 0018-9294. (doi:10.1109/TBME.2014.2299772) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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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.

Item Type: Article
DOI/Identification number: 10.1109/TBME.2014.2299772
Uncontrolled keywords: Body sensor networks, e-AR sensor, gait, longest common subsequence (LCSS), singular spectrum analysis (SSA)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Delaram Jarchi
Date Deposited: 18 Oct 2018 11:24 UTC
Last Modified: 29 May 2019 21:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69640 (The current URI for this page, for reference purposes)
Jarchi, Delaram: https://orcid.org/0000-0001-6699-8721
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