Neuro-Based Thumb-Tip Force and Joint Angle Modelling for Development of Prosthetic Thumb Control

Human fingers have a specific role that contributes to different hand functions. Among these fingers, the thumb plays the most special function as an anchor to many hand activities. As a result, the loss of the thumb due to traumatic accidents can be catastrophic as proper hand function will be seve...

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Bibliographic Details
Published in:International journal of advanced robotic systems 2013-10, Vol.10 (10)
Main Authors: Jalaludin, Nor Anija, Sidek, Shahrul Na'im, Shamsudin, Abu Ubaidah
Format: Article
Language:English
Subjects:
Accidents
Amputation
Artificial neural networks
Configuration management
Configurations
Controllers
Fingers & toes
Muscles
Nervous system
Neural networks
Prostheses
Researchers
Signal classification
Wavelet transforms
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Summary:Human fingers have a specific role that contributes to different hand functions. Among these fingers, the thumb plays the most special function as an anchor to many hand activities. As a result, the loss of the thumb due to traumatic accidents can be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. The movement of the prosthetic device can be naturally controlled by using electromyogram (EMG) signals. In this work, the EMG signals from the human muscles were measured in different thumb configurations and thumb-tip forces in flexion movement. The muscles involved are the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). The classification of the EMG signals based on different force and thumb configurations is performed using an Artificial Neural Network (ANN). From a series of experiments, the results show that the neural network efficiently classified the signals and a unique set of EMG signals was generated for each thumb movement and force. Therefore, EMG signals were used to control the prosthetic movement with aid from the developed neural network.
ISSN:1729-8806
1729-8814
DOI:10.5772/56666