Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Moon, Kee S., Lee, Sung Q, Ozturk, Yusuf, Gaidhani, Apoorva, Cox, Jeremiah A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
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
_version_ 1783475903007817728
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
work_keys_str_mv AT moonkees identificationofgaitmotionpatternsusingwearableinertialsensornetwork
AT leesungq identificationofgaitmotionpatternsusingwearableinertialsensornetwork
AT ozturkyusuf identificationofgaitmotionpatternsusingwearableinertialsensornetwork
AT gaidhaniapoorva identificationofgaitmotionpatternsusingwearableinertialsensornetwork
AT coxjeremiaha identificationofgaitmotionpatternsusingwearableinertialsensornetwork