User Identification from Gait Analysis Using Multi-Modal Sensors in Smart Insoleopen access
- Authors
- Choi, S.-I.; Moon, J.; Park, H.-C.; Choi, S.T.
- Issue Date
- Sep-2019
- Publisher
- NLM (Medline)
- Keywords
- gait analysis; linear discriminant analysis; multi-modal feature; multi-modal sensors; smart insole; user identification; wearable sensor
- Citation
- Sensors (Basel, Switzerland), v.19, no.17
- Journal Title
- Sensors (Basel, Switzerland)
- Volume
- 19
- Number
- 17
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/38997
- DOI
- 10.3390/s19173785
- ISSN
- 1424-8220
1424-8220
- Abstract
- Recent studies indicate that individuals can be identified by their gait pattern. A number of sensors including vision, acceleration, and pressure have been used to capture humans' gait patterns, and a number of methods have been developed to recognize individuals from their gait pattern data. This study proposes a novel method of identifying individuals using null-space linear discriminant analysis on humans' gait pattern data. The gait pattern data consists of time series pressure and acceleration data measured from multi-modal sensors in a smart insole used while walking. We compare the identification accuracies from three sensing modalities, which are acceleration, pressure, and both in combination. Experimental results show that the proposed multi-modal features identify 14 participants with high accuracy over 95% from their gait pattern data of walking.
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Collections - College of Engineering > School of Mechanical Engineering > 1. Journal Articles
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