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Adapting myoelectric control in real-time using a virtual environment
by
Woodward, Richard B
, Hargrove, Levi J
in
Adaptation
/ Adult
/ Amputation
/ Amputee
/ Artificial Limbs
/ Classification
/ Classifiers
/ Computation
/ Computer applications
/ Controllers
/ Data collection
/ Electromyography
/ Electromyography - methods
/ Environments
/ Female
/ Humans
/ Interactive control
/ Machine Learning
/ Male
/ Movement - physiology
/ Myoelectric control
/ Myoelectricity
/ Pattern recognition
/ Pattern recognition systems
/ Pattern Recognition, Automated - methods
/ Performance enhancement
/ Posture
/ Process controls
/ Prostheses
/ Prostheses and implants
/ Prosthetics
/ Real time
/ Technology
/ Training
/ Upper-limb prostheses
/ User needs
/ Virtual environments
/ Virtual guided training
/ Virtual Reality
/ Virtual rehabilitation
/ Young Adult
2019
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Adapting myoelectric control in real-time using a virtual environment
by
Woodward, Richard B
, Hargrove, Levi J
in
Adaptation
/ Adult
/ Amputation
/ Amputee
/ Artificial Limbs
/ Classification
/ Classifiers
/ Computation
/ Computer applications
/ Controllers
/ Data collection
/ Electromyography
/ Electromyography - methods
/ Environments
/ Female
/ Humans
/ Interactive control
/ Machine Learning
/ Male
/ Movement - physiology
/ Myoelectric control
/ Myoelectricity
/ Pattern recognition
/ Pattern recognition systems
/ Pattern Recognition, Automated - methods
/ Performance enhancement
/ Posture
/ Process controls
/ Prostheses
/ Prostheses and implants
/ Prosthetics
/ Real time
/ Technology
/ Training
/ Upper-limb prostheses
/ User needs
/ Virtual environments
/ Virtual guided training
/ Virtual Reality
/ Virtual rehabilitation
/ Young Adult
2019
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Do you wish to request the book?
Adapting myoelectric control in real-time using a virtual environment
by
Woodward, Richard B
, Hargrove, Levi J
in
Adaptation
/ Adult
/ Amputation
/ Amputee
/ Artificial Limbs
/ Classification
/ Classifiers
/ Computation
/ Computer applications
/ Controllers
/ Data collection
/ Electromyography
/ Electromyography - methods
/ Environments
/ Female
/ Humans
/ Interactive control
/ Machine Learning
/ Male
/ Movement - physiology
/ Myoelectric control
/ Myoelectricity
/ Pattern recognition
/ Pattern recognition systems
/ Pattern Recognition, Automated - methods
/ Performance enhancement
/ Posture
/ Process controls
/ Prostheses
/ Prostheses and implants
/ Prosthetics
/ Real time
/ Technology
/ Training
/ Upper-limb prostheses
/ User needs
/ Virtual environments
/ Virtual guided training
/ Virtual Reality
/ Virtual rehabilitation
/ Young Adult
2019
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Adapting myoelectric control in real-time using a virtual environment
Journal Article
Adapting myoelectric control in real-time using a virtual environment
2019
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Overview
Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device.
Here we present an engaging and interactive virtual reality environment for optimal training of a myoelectric controller. Using this tool, we evaluated the importance of training a classifier actively (i.e., moving the residual limb during data collection) compared to passively (i.e., maintaining the limb in a single, neutral orientation), and whether computational adaptation through serious gaming can improve performance.
We found that actively trained classifiers performed significantly better than passively trained classifiers for non-amputees (P < 0.05). Furthermore, collecting data passively with minimal instruction, paired with computational adaptation in a virtual environment, significantly improved real-time performance of myoelectric controllers.
These results further support previous work which suggested active movements during data collection can improve pattern recognition systems. Furthermore, adaptation within a virtual guided serious game environment can improve real-time performance of myoelectric controllers.
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