DocumentCode
496191
Title
Developing a motion language: Gait analysis from accelerometer sensor systems
Author
Anna, Anita Sant ; Wickströ, Nicholas
Author_Institution
Sch. of Informations Sci., Electr. & Comput. Eng., Halmstad Univ., Halmstad, Sweden
fYear
2009
fDate
1-3 April 2009
Firstpage
1
Lastpage
8
Abstract
This work concerns the use of human movement classification as a tool for monitoring and supporting older peoples´ lives. The “motion language” methodology is a movement classification technique which aims at generalizing movements and providing easy interpretation of motion signals by decomposing activities into elementary building blocks referred to as “motion primitives”. The use of motion primitives to classify motion from visual data has been studied. This work shows that the motion language methodology can be applied to acceleration signals, contributing to the development of wearable monitoring systems. This paper explains the development of the motion language and its use in a gait analysis study. Preliminary results show that the motion language methodology can be used to quantitatively measure gait parameters. In addition, motion primitives are shown to express static and dynamic characteristics of different gait patterns and were used to calculate a new symmetry index.
Keywords
accelerometers; gait analysis; handicapped aids; patient monitoring; pattern classification; accelerometer sensor systems; gait analysis; human movement classification; motion language; motion signals; older peoples lives; wearable monitoring systems; Acceleration; Accelerometers; Aging; Biomedical monitoring; Europe; Information analysis; Medical services; Motion analysis; Motion measurement; Sensor systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth 2009. 3rd International Conference on
Conference_Location
London
Print_ISBN
978-963-9799-42-4
Electronic_ISBN
978-963-9799-30-1
Type
conf
DOI
10.4108/ICST.PERVASIVEHEALTH2009.5913
Filename
5191184
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