Abstract

The physiological condition of a person may affect his/her daily behaviour such as gait or posture. For example under the fatigue condition, a person may be used to walk in a slower pace than usual. This paper presents a novel gait analysis approach to detect movement variations such as walking pace or speed change, walking with bending, walking with heavy breath, arm or leg swing change. Based on the geometry of the silhouette, we segment the body to five main parts including head, upper body, lower body, arms and legs. For a specific analysis, we segment the torso to upper and lower body. For the walking pace analysis, we use the leg movement in the lower body to find the max distance in a pace cycle and corresponding pace speed. The angles between the head or upper body and the vertical line are used to detect the walking with bending or walking with breathing. The arm swing angle or pace variation during walking can also be detected. We compare the normal condition with other abnormal condition such as people who have respiratory obstruction leading to heavy breathing, and have stomach ache resulting humpbacked status. These cause the angle of upper body different with normal condition, so we can observe these signals to give a warning notice. Our experiments show that with these fine posture features, we are able to detect a person's gait change. Examples are that a person is humpbacked, or the arm/leg swing and pace distance are in abnormal rhythm. From our gait analysis approach, we observe that when people are in a tired condition, they are used to adopt a static and comfortable pace distance to walk in our experimental results.

Original languageEnglish
Title of host publication2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOIs
Publication statusPublished - 2013
Event2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan
Duration: Oct 29 2013Nov 1 2013

Other

Other2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
CountryTaiwan
CityKaohsiung
Period10/29/1311/1/13

Fingerprint

Gait analysis
Gravitation
Fatigue of materials
Geometry
Experiments

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

Cite this

Tsao, Y. F., Liu, W. T., & Chiu, C. T. (2013). Human gait analysis by body segmentation and center of gravity. In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 [6694128] https://doi.org/10.1109/APSIPA.2013.6694128

Human gait analysis by body segmentation and center of gravity. / Tsao, Ying Fang; Liu, Wen Te; Chiu, Ching Te.

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. 2013. 6694128.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tsao, YF, Liu, WT & Chiu, CT 2013, Human gait analysis by body segmentation and center of gravity. in 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013., 6694128, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013, Kaohsiung, Taiwan, 10/29/13. https://doi.org/10.1109/APSIPA.2013.6694128
Tsao YF, Liu WT, Chiu CT. Human gait analysis by body segmentation and center of gravity. In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. 2013. 6694128 https://doi.org/10.1109/APSIPA.2013.6694128
Tsao, Ying Fang ; Liu, Wen Te ; Chiu, Ching Te. / Human gait analysis by body segmentation and center of gravity. 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. 2013.
@inproceedings{21d998d829cb416ebc810a777e672078,
title = "Human gait analysis by body segmentation and center of gravity",
abstract = "The physiological condition of a person may affect his/her daily behaviour such as gait or posture. For example under the fatigue condition, a person may be used to walk in a slower pace than usual. This paper presents a novel gait analysis approach to detect movement variations such as walking pace or speed change, walking with bending, walking with heavy breath, arm or leg swing change. Based on the geometry of the silhouette, we segment the body to five main parts including head, upper body, lower body, arms and legs. For a specific analysis, we segment the torso to upper and lower body. For the walking pace analysis, we use the leg movement in the lower body to find the max distance in a pace cycle and corresponding pace speed. The angles between the head or upper body and the vertical line are used to detect the walking with bending or walking with breathing. The arm swing angle or pace variation during walking can also be detected. We compare the normal condition with other abnormal condition such as people who have respiratory obstruction leading to heavy breathing, and have stomach ache resulting humpbacked status. These cause the angle of upper body different with normal condition, so we can observe these signals to give a warning notice. Our experiments show that with these fine posture features, we are able to detect a person's gait change. Examples are that a person is humpbacked, or the arm/leg swing and pace distance are in abnormal rhythm. From our gait analysis approach, we observe that when people are in a tired condition, they are used to adopt a static and comfortable pace distance to walk in our experimental results.",
author = "Tsao, {Ying Fang} and Liu, {Wen Te} and Chiu, {Ching Te}",
year = "2013",
doi = "10.1109/APSIPA.2013.6694128",
language = "English",
isbn = "9789869000604",
booktitle = "2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013",

}

TY - GEN

T1 - Human gait analysis by body segmentation and center of gravity

AU - Tsao, Ying Fang

AU - Liu, Wen Te

AU - Chiu, Ching Te

PY - 2013

Y1 - 2013

N2 - The physiological condition of a person may affect his/her daily behaviour such as gait or posture. For example under the fatigue condition, a person may be used to walk in a slower pace than usual. This paper presents a novel gait analysis approach to detect movement variations such as walking pace or speed change, walking with bending, walking with heavy breath, arm or leg swing change. Based on the geometry of the silhouette, we segment the body to five main parts including head, upper body, lower body, arms and legs. For a specific analysis, we segment the torso to upper and lower body. For the walking pace analysis, we use the leg movement in the lower body to find the max distance in a pace cycle and corresponding pace speed. The angles between the head or upper body and the vertical line are used to detect the walking with bending or walking with breathing. The arm swing angle or pace variation during walking can also be detected. We compare the normal condition with other abnormal condition such as people who have respiratory obstruction leading to heavy breathing, and have stomach ache resulting humpbacked status. These cause the angle of upper body different with normal condition, so we can observe these signals to give a warning notice. Our experiments show that with these fine posture features, we are able to detect a person's gait change. Examples are that a person is humpbacked, or the arm/leg swing and pace distance are in abnormal rhythm. From our gait analysis approach, we observe that when people are in a tired condition, they are used to adopt a static and comfortable pace distance to walk in our experimental results.

AB - The physiological condition of a person may affect his/her daily behaviour such as gait or posture. For example under the fatigue condition, a person may be used to walk in a slower pace than usual. This paper presents a novel gait analysis approach to detect movement variations such as walking pace or speed change, walking with bending, walking with heavy breath, arm or leg swing change. Based on the geometry of the silhouette, we segment the body to five main parts including head, upper body, lower body, arms and legs. For a specific analysis, we segment the torso to upper and lower body. For the walking pace analysis, we use the leg movement in the lower body to find the max distance in a pace cycle and corresponding pace speed. The angles between the head or upper body and the vertical line are used to detect the walking with bending or walking with breathing. The arm swing angle or pace variation during walking can also be detected. We compare the normal condition with other abnormal condition such as people who have respiratory obstruction leading to heavy breathing, and have stomach ache resulting humpbacked status. These cause the angle of upper body different with normal condition, so we can observe these signals to give a warning notice. Our experiments show that with these fine posture features, we are able to detect a person's gait change. Examples are that a person is humpbacked, or the arm/leg swing and pace distance are in abnormal rhythm. From our gait analysis approach, we observe that when people are in a tired condition, they are used to adopt a static and comfortable pace distance to walk in our experimental results.

UR - http://www.scopus.com/inward/record.url?scp=84893210558&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893210558&partnerID=8YFLogxK

U2 - 10.1109/APSIPA.2013.6694128

DO - 10.1109/APSIPA.2013.6694128

M3 - Conference contribution

AN - SCOPUS:84893210558

SN - 9789869000604

BT - 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013

ER -