DistaNet: grasp-specific distance biofeedback promotes the retention of myoelectric skills.
J Neural Eng
; 21(3)2024 Jun 11.
Article
in En
| MEDLINE
| ID: mdl-38742365
ABSTRACT
Objective.An active myoelectric interface responds to the user's muscle signals to enable movements. Machine learning can decode user intentions from myoelectric signals. However, machine learning-based interface control lacks continuous, intuitive feedback about task performance, needed to facilitate the acquisition and retention of myoelectric control skills.Approach.We propose DistaNet as a neural network-based framework that extracts smooth, continuous, and low-dimensional signatures of the hand grasps from multi-channel myoelectric signals and provides grasp-specific biofeedback to the users.Main results.Experimental results show its effectiveness in decoding user gestures and providing biofeedback, helping users retain the acquired motor skills.Significance.We demonstrates myoelectric skill retention in a pattern recognition setting for the first time.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Biofeedback, Psychology
/
Hand Strength
/
Electromyography
Limits:
Adult
/
Female
/
Humans
/
Male
Language:
En
Journal:
J Neural Eng
Journal subject:
NEUROLOGIA
Year:
2024
Document type:
Article
Affiliation country:
United kingdom
Country of publication:
United kingdom