Design and Validation of a Morphing Myoelectric Hand Posture Controller Based on Principal Component Analysis of Human Grasping

An ideal myoelectric prosthetic hand should have the ability to continuously morph between any posture like an anatomical hand. This paper describes the design and validation of a morphing myoelectric hand controller based on principal component analysis of human grasping. The controller commands co...

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Bibliographic Details
Published inIEEE transactions on neural systems and rehabilitation engineering Vol. 22; no. 2; pp. 249 - 257
Main Authors Segil, Jacob L., Weir, Richard F. ff
Format Journal Article
LanguageEnglish
Published United States IEEE 01.03.2014
Subjects
Adult
Algorithms
Amputation
Biomechatronics
Correlation
Electrodes
Electromyography
electromyography (EMG)
Electromyography - instrumentation
Female
Filtering
Finite Element Analysis
Grasping
Hand
Hand Strength - physiology
Humans
Joints - anatomy & histology
Joints - physiology
Male
Measurement
Middle Aged
Models, Statistical
myoelectric control
Pattern Recognition, Automated
Posture - physiology
Principal Component Analysis
Prostheses and Implants
Prosthesis Design
Range of Motion, Articular
Reproducibility of Results
Transforms
transradial prosthesis
User-Computer Interface
Young Adult
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Summary:An ideal myoelectric prosthetic hand should have the ability to continuously morph between any posture like an anatomical hand. This paper describes the design and validation of a morphing myoelectric hand controller based on principal component analysis of human grasping. The controller commands continuously morphing hand postures including functional grasps using between two and four surface electromyography (EMG) electrodes pairs. Four unique maps were developed to transform the EMG control signals in the principal component domain. A preliminary validation experiment was performed by 10 nonamputee subjects to determine the map with highest performance. The subjects used the myoelectric controller to morph a virtual hand between functional grasps in a series of randomized trials. The number of joints controlled accurately was evaluated to characterize the performance of each map. Additional metrics were studied including completion rate, time to completion, and path efficiency. The highest performing map controlled over 13 out of 15 joints accurately.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2013.2260172