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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Won-Il Park Sun-Cheol Kwon Hae-Dong Lee Jung Kim |
| Copyright Year | 2009 |
| Description | Author affiliation: Robotics program, KAIST, Daejeon, Korea (Won-Il Park; Jung Kim) || Department of Mechanical Engineering, KAIST, Daejeon, Korea (Sun-Cheol Kwon) || BK21 Mechatronics Group, Chungnam National University, Daejeon, Korea (Hae-Dong Lee) |
| Abstract | Due to the difficulties in measurement of muscle activities and the complex musculoskeletal structure, estimations of the thumb-tip force in real time have been a challenge for controlling artificial prosthesis naturally. This study describes an isometric thumb-tip force estimation technique based on phenomenological muscle model named Hill's model. The surface electromyogram (sEMG) signals of the muscles near surface were measured and converted to muscle activation information. The activations of deep muscles were inferred from the ratios of muscle activations from earlier study. The muscle length of each contributed muscle was obtained by using motion capture system and musculoskeletal modeling software packages. Once muscle forces were calculated, thumb-tip force was estimated based on mapping model from the muscle force to thumb-tip force. The proposed method was evaluated in comparisons with an artificial neural network (ANN) under four different thumb configurations to investigate the potential for estimations under conditions in which the thumb configuration changes. The results seem to be promising and the proposed method could be applied to predict finger-tip forces from non-invasive neurosignals with a real-time prosthesis control system. |
| Starting Page | 305 |
| Ending Page | 310 |
| File Size | 701817 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424437887 |
| ISSN | 19457898 |
| DOI | 10.1109/ICORR.2009.5209518 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-23 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Real time systems Musculoskeletal system Force measurement Software packages Fingers Thumb Artificial neural networks Muscles Force control Prosthetics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Rehabilitation Control and Systems Engineering Electrical and Electronic Engineering |
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