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Modelling and analysis of hand motion in everyday activities with application to prosthetic hand technology
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Thornton, Callum John (2023) Modelling and analysis of hand motion in everyday activities with application to prosthetic hand technology. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b3910244
Abstract
Upper-limb prostheses are either too expensive for many consumers or exhibit a greatly simplified choice of actions, this research aims to enable an improvement in the quality of life for recipients of these devices. Previous attempts at determining the hand shapes performed during activities of daily living (ADL) provide a limited range of tasks studied and data recorded. To avoid these limitations, motion capture systems and machine learning techniques have been utilised throughout this study. A portable motion capture system created, utilising a Leap Motion controller (LMC), has captured natural hand motions during modern ADL. Furthering the use of these data, a method applying optimisation techniques alongside a musculoskeletal model of the hand is proposed for predicting muscle excitations from kinematic data. The LMC was also employed in a device (AirGo) created to measure joint angles, aiming to provide an improvement to joint angle measurements in hand clinics. Hand movements for 22 participants were recorded during ADL over 111 hours and 20 minutes - providing a taxonomy of 40 and 24 hand shapes for the left and right hands, respectively. The predicted muscle excitations produced joint angles with an average correlation of 0.58 to those of the desired hand shapes. AirGo has been successfully employed within a hand therapy clinic to measure digit angles of 11 patients. A taxonomy of the hand shapes used in modern ADL is presented, highlighting the hand shapes currently more appropriate to consider during upper-limb prostheses development. A method for predicting the muscle excitations of the hand from kinematic data is introduced, implemented with data collected during ADL. AirGo offered improved repeatability over traditional devices used for such measurements with greater ease of use.
Item Type: | Thesis (PhD) | ||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QP Physiology R Medicine > RD Surgery T Technology > TA Engineering (General). Civil engineering (General) |
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Library of Congress Subject Headings (LCSH): | Artificial arms, Artificial hands, Prosthesis, Hand -- Movements, Kinematics -- Data processing, Machine learning | ||||
Official Date: | January 2023 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | School of Engineering | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Chappell, Michael J. ; Evans, Neil D. ; Hardwicke, Joseph | ||||
Format of File: | |||||
Extent: | xix, 250, A16, B9, C6 pages : illustrations, charts | ||||
Language: | eng |
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