Feature extraction from ear-worn sensor data for gait analysis

Li, Ling and Atallah, Louis and Lo, Benny and Yang, Guang-Zhong (2014) Feature extraction from ear-worn sensor data for gait analysis. In: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE pp. 560-563. (doi:https://doi.org/10.1109/BHI.2014.6864426) (Full text available)

Abstract

Gait analysis has a significant role in assessing human's walking pattern. It is generally used in sports science for understanding body mechanics, and it is also used to monitor patients' neuro-disorder related gait abnormalities. Traditional marker-based systems are well known for tracking gait parameters for gait analysis, however, it requires long set up time therefore very difficult to be applied in everyday realtime monitoring. Nowadays, there is ever growing of interest in developing portable devices and their supporting software with novel algorithms for gait pattern analysis. The aim of this research is to investigate the possibilities of novel gait pattern detection algorithms for accelerometer-based sensors. In particular, we have used e-AR sensor, an ear-worn sensor which registers body motion via its embedded 3-D accelerom-eter. Gait data was given semantic annotation using pressure mat as well as real-time video recording. Important time stamps within a gait cycle, which are essential for extracting meaningful gait parameters, were identified. Furthermore, advanced signal processing algorithm was applied to perform automatic feature extraction by signal decomposition and reconstruction. Analysis on real-word data has demonstrated the potential for an accelerometer-based sensor system and its ability to extract of meaningful gait parameters.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: Wearable systems and sensors
Subjects: Q Science
T Technology
Divisions: Faculties > Sciences > School of Computing > Computing Education Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Caroline Li
Date Deposited: 17 Jul 2015 16:05 UTC
Last Modified: 17 Jan 2017 20:51 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/49592 (The current URI for this page, for reference purposes)
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