Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review
Hands are central to nearly every aspect of daily life, so losing an upper limb due to amputation can severely affect a person’s independence. Robotic prostheses offer a promising solution by mimicking many of the functions of a natural arm, leading to an increasing need for advanced prosthetic desi...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/3892 |
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| author | Beibit Abdikenov Darkhan Zholtayev Kanat Suleimenov Nazgul Assan Kassymbek Ozhikenov Aiman Ozhikenova Nurbek Nadirov Akim Kapsalyamov |
| author_facet | Beibit Abdikenov Darkhan Zholtayev Kanat Suleimenov Nazgul Assan Kassymbek Ozhikenov Aiman Ozhikenova Nurbek Nadirov Akim Kapsalyamov |
| author_sort | Beibit Abdikenov |
| collection | DOAJ |
| description | Hands are central to nearly every aspect of daily life, so losing an upper limb due to amputation can severely affect a person’s independence. Robotic prostheses offer a promising solution by mimicking many of the functions of a natural arm, leading to an increasing need for advanced prosthetic designs. However, developing an effective robotic hand prosthesis is far from straightforward. It involves several critical steps, including creating accurate models, choosing materials that balance biocompatibility with durability, integrating electronic and sensory components, and perfecting control systems before final production. A key factor in ensuring smooth, natural movements lies in the method of control. One popular approach is to use electromyography (EMG), which relies on electrical signals from the user’s remaining muscle activity to direct the prosthesis. By decoding these signals, we can predict the intended hand and arm motions and translate them into real-time actions. Recent strides in machine learning have made EMG-based control more adaptable, offering users a more intuitive experience. Alongside this, researchers are exploring tactile sensors for enhanced feedback, materials resilient in harsh conditions, and mechanical designs that better replicate the intricacies of a biological limb. This review brings together these advancements, focusing on emerging trends and future directions in robotic upper-limb prosthesis development. |
| format | Article |
| id | doaj-art-a3e4a34f7a1d44b7b1cccd1ea7d73f6d |
| institution | Matheson Library |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a3e4a34f7a1d44b7b1cccd1ea7d73f6d2025-07-11T14:42:51ZengMDPI AGSensors1424-82202025-06-012513389210.3390/s25133892Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive ReviewBeibit Abdikenov0Darkhan Zholtayev1Kanat Suleimenov2Nazgul Assan3Kassymbek Ozhikenov4Aiman Ozhikenova5Nurbek Nadirov6Akim Kapsalyamov7Science and Innovation Center “Artificial Intelligence”, Astana IT University, Astana 010000, KazakhstanScience and Innovation Center “Artificial Intelligence”, Astana IT University, Astana 010000, KazakhstanReLive Research, Astana 010000, KazakhstanReLive Research, Astana 010000, KazakhstanInstitute of Automation and Information Technologies, Satbayev University, Almaty 050000, KazakhstanInstitute of Automation and Information Technologies, Satbayev University, Almaty 050000, KazakhstanReLive Research, Astana 010000, KazakhstanFaculty of Engineering and Mathematics, Hochschule Bielefeld, 33619 Bielefeld, GermanyHands are central to nearly every aspect of daily life, so losing an upper limb due to amputation can severely affect a person’s independence. Robotic prostheses offer a promising solution by mimicking many of the functions of a natural arm, leading to an increasing need for advanced prosthetic designs. However, developing an effective robotic hand prosthesis is far from straightforward. It involves several critical steps, including creating accurate models, choosing materials that balance biocompatibility with durability, integrating electronic and sensory components, and perfecting control systems before final production. A key factor in ensuring smooth, natural movements lies in the method of control. One popular approach is to use electromyography (EMG), which relies on electrical signals from the user’s remaining muscle activity to direct the prosthesis. By decoding these signals, we can predict the intended hand and arm motions and translate them into real-time actions. Recent strides in machine learning have made EMG-based control more adaptable, offering users a more intuitive experience. Alongside this, researchers are exploring tactile sensors for enhanced feedback, materials resilient in harsh conditions, and mechanical designs that better replicate the intricacies of a biological limb. This review brings together these advancements, focusing on emerging trends and future directions in robotic upper-limb prosthesis development.https://www.mdpi.com/1424-8220/25/13/3892controlEMG signal processingmachine learningprosthetic materialsrobotic hand prosthesistactile sensing |
| spellingShingle | Beibit Abdikenov Darkhan Zholtayev Kanat Suleimenov Nazgul Assan Kassymbek Ozhikenov Aiman Ozhikenova Nurbek Nadirov Akim Kapsalyamov Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review Sensors control EMG signal processing machine learning prosthetic materials robotic hand prosthesis tactile sensing |
| title | Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review |
| title_full | Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review |
| title_fullStr | Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review |
| title_full_unstemmed | Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review |
| title_short | Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review |
| title_sort | emerging frontiers in robotic upper limb prostheses mechanisms materials tactile sensors and machine learning based emg control a comprehensive review |
| topic | control EMG signal processing machine learning prosthetic materials robotic hand prosthesis tactile sensing |
| url | https://www.mdpi.com/1424-8220/25/13/3892 |
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