NDLI: Human Motion Regeneration using Sensors and Vision

Content Provider	IEEE Xplore Digital Library
Author	Senanayake, S. Gopalai, A.A.
Copyright Year	2008
Description	Author affiliation: Sch. of Eng., Monash Univ. Sunway Campus, Bandar Sunway (Senanayake, S.; Gopalai, A.A.)
Abstract	This paper explores the opportunity of using tri-axial wireless accelerometers and optical camera system for human motion regeneration. A motion analysis system based on stick figures and musculoskeletal model is developed. Accelerometers are placed on joints of human body and markers are placed on the similar joints for the vision system. Data are acquired from these sensors in soft-real time using virtual instrumentation; the acquired data is then conditioned and converted into required parameters for motion regeneration. In vision based data acquiring is based on motion capture (MOCAP) data which is required during both the modeling and simulation process of the musculoskeletal models. While sensor based motion regeneration in this paper deals with the motion of the human body segment in the X and Y direction, looking into the motion of the anterior/posterior and lateral directions respectively, vision based motion regeneration addresses three dimensional full body musculoskeletal model. In order to regenerate specific human motion, lawn bowling action and soccer actions have been selected during the simulation.
Starting Page	1032
Ending Page	1037
File Size	5718695
Page Count	6
File Format	PDF
ISBN	9781424416752
DOI	10.1109/RAMECH.2008.4681465
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Biological system modeling Musculoskeletal system Accelerometers Joints Wireless sensor networks Optical sensors Cameras Repeaters Motion analysis soft-real time stick figure animation Motion Capture Data Musculoskeletel model
Content Type	Text
Resource Type	Article

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