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Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network
Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891807/ https://www.ncbi.nlm.nih.gov/pubmed/31752136 http://dx.doi.org/10.3390/s19225024 |
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author | Moon, Kee S. Lee, Sung Q Ozturk, Yusuf Gaidhani, Apoorva Cox, Jeremiah A. |
author_facet | Moon, Kee S. Lee, Sung Q Ozturk, Yusuf Gaidhani, Apoorva Cox, Jeremiah A. |
author_sort | Moon, Kee S. |
collection | PubMed |
description | Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns. |
format | Online Article Text |
id | pubmed-6891807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68918072019-12-23 Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network Moon, Kee S. Lee, Sung Q Ozturk, Yusuf Gaidhani, Apoorva Cox, Jeremiah A. Sensors (Basel) Article Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns. MDPI 2019-11-18 /pmc/articles/PMC6891807/ /pubmed/31752136 http://dx.doi.org/10.3390/s19225024 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Moon, Kee S. Lee, Sung Q Ozturk, Yusuf Gaidhani, Apoorva Cox, Jeremiah A. Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network |
title | Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network |
title_full | Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network |
title_fullStr | Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network |
title_full_unstemmed | Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network |
title_short | Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network |
title_sort | identification of gait motion patterns using wearable inertial sensor network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891807/ https://www.ncbi.nlm.nih.gov/pubmed/31752136 http://dx.doi.org/10.3390/s19225024 |
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