Kinematic and Muscle Synergies in Grasping Hand
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Author/Creator ORCID
Date
2021-01-01
Type of Work
Department
Computer Science and Electrical Engineering
Program
Computer Science
Citation of Original Publication
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Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
Distribution Rights granted to UMBC by the author.
Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
Subjects
Abstract
The human hand is a multidimensional robot that can execute complex functions withincredible ease. It is hypothesized that the central nervous system (CNS) achieves this
by controlling in low dimensional space of movement primitives also known as
synergies. Kinematic and muscle synergies have been extensively studied, but only a
few studies have focused on both. In this theses, kinematic and muscle synergies were
extracted from the hand grasps often used in the activities of daily living using principal
component analysis (PCA). Movements were then reconstructed as weighted linear
combinations of synergies. The results indicated that optimal command signals
originating from the CNS to achieve a successful grasp might be reflected in muscles
and kinematics. Also, to understand the interplay between kinematic and muscle
synergies, musculoskeletal synergies were extracted using PCA and data fusion
methods. The research findings and methods presented here might enable improved
control of neuro-prosthetics.