Abstract
The use of prostheses is necessary to restore lost limbs to a level of functionality to enable activity of daily living. Many prostheses are now using myoelectric based control techniques to operate. However, to develop a model based controller for the system remains a challenge as accurate model is necessary. This study investigates the use of electromyogram and ultrasound signal to predict thumb tip force based on Hill’s Muscle model. The results obtained has shown a significant improvement in the prediction of thumb tip force as much as 31.45% of average RMSE over the benchmark model that leverages on biomechanics model and active marker to characterize the muscle.
Highlights
Over the last two centuries, the industrial era has grown significantly
In order to have simultaneous, real-time data recorded for the thumb-tip force, thumb flexion angle, electromyogram and ultrasound signals from the muscles, the platform is interfaced to a computer using MATLAB/Simulink software and Arduino board that serves as data acquisition unit. 2.3 EMG measurement In this study, four thumb muscles were selected namely Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and the First Dorsal Interosseous (FDI)
The Particle Swarm Optimization (PSO) technique is used to find the best solution to the unknown constants and is applied to the Hill’s muscle model using muscle activation data which is generated from the EMG signal as well as the recorded muscle length data
Summary
Can result in severe infection and ulceration, which may lead to amputation.[3]. Most are controlled using a set of functions that can change the movement of all fingers into a set position or mode This allows the amputee to use the prosthetic hand for certain work activities, by helping to grip an object by releasing and manipulating buttons.[11] The set of functions can help with numerous activities, but the prosthetic hand is limited on what the amputee can do to a full extent. The following section will present the result of signal measurement in term of its contribution in predicting the thumb tip force using Hill’s muscle model
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