Smart Prosthetics Controller Types: Review

Advanced prosthetics are a crucial aspect of rehabilitation technology and are receiving increased attention globally. Approximately 2 million people require prosthetic limbs, presenting opportunities for enhancing their quality of life. State-of-the-art technologies such as realistic arms and myoelectric prostheses are gaining popularity. Progress in sensor technology, artificial intelligence, and materials has driven the field forward. Various types of controllers, including direct, pattern recognition, and proportional-derivative, have been developed. Integration of material science, computer science, artificial intelligence, and neurology has facilitated controller advancements. Techniques like targeted muscle reinnervation and Osseo integrated prostheses offer improved surgical options. Gesture recognition technologies and intelligent sensors are enhancing hand control. Future advancements will involve machine learning, artificial intelligence, and sensing techniques, while ethical concerns must be addressed. Advanced myoelectric prostheses, also known as myocontrolled or lower-limb micromod investigative prostheses, have a patient acceptance rate of 75% to 80%. However, while these methods offer advantages, there are also drawbacks. Integrating different types of controllers for these smart prostheses and enhancing the overall device's strength and robustness will have a significant impact. This discussion focuses on various types of smart prosthetic controllers, dividing muscle activity into extracellular myoelectric potential and EEG signals

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Publication Details

Journal
Anbar journal for engineering sciences/Mağallaẗ al-anbār li-l-ʻulūm al-handasiyyaẗ
Published
2024-12-15
DOI
https://doi.org/10.37649/aengs.2024.152744.1097
Citations
1
Primary Topic
Muscle activation and electromyography studies
Type
article
Field-Weighted Citation Impact
0.18
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article

Smart Prosthetics Controller Types: Review

Ali Ahmed, Yousif Ismail Mohammed Al Mashhadany, R. Badlishah Ahmad, Falah khaleefah
1 citations
Anbar journal for engineering sciences/Mağallaẗ al-anbār li-l-ʻulūm al-handasiyyaẗ
Muscle activation and electromyography studies
0.18
article

Smart Prosthetics Controller Types: Review

Ali Ahmed, Yousif Ismail Mohammed Al Mashhadany, R. Badlishah Ahmad, Falah khaleefah
article en
1 citations

Abstract

Advanced prosthetics are a crucial aspect of rehabilitation technology and are receiving increased attention globally. Approximately 2 million people require prosthetic limbs, presenting opportunities for enhancing their quality of life. State-of-the-art technologies such as realistic arms and myoelectric prostheses are gaining popularity. Progress in sensor technology, artificial intelligence, and materials has driven the field forward. Various types of controllers, including direct, pattern recognition, and proportional-derivative, have been developed. Integration of material science, computer science, artificial intelligence, and neurology has facilitated controller advancements. Techniques like targeted muscle reinnervation and Osseo integrated prostheses offer improved surgical options. Gesture recognition technologies and intelligent sensors are enhancing hand control. Future advancements will involve machine learning, artificial intelligence, and sensing techniques, while ethical concerns must be addressed. Advanced myoelectric prostheses, also known as myocontrolled or lower-limb micromod investigative prostheses, have a patient acceptance rate of 75% to 80%. However, while these methods offer advantages, there are also drawbacks. Integrating different types of controllers for these smart prostheses and enhancing the overall device's strength and robustness will have a significant impact. This discussion focuses on various types of smart prosthetic controllers, dividing muscle activity into extracellular myoelectric potential and EEG signals

Anbar journal for engineering sciences/Mağallaẗ al-anbār li-l-ʻulūm al-handasiyyaẗVol. 15(2)
University of Anbar (IQ), Universiti Malaysia Perlis (MY)
Affordable and clean energy
Openalex Percentile: Top 49%
Muscle activation and electromyography studies
0.18
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