Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review

Abdikenov B. Zholtayev D. Suleimenov K. Assan N. Ozhikenov K. Ozhikenova A. Nadirov N. Kapsalyamov A.
July 2025 Multidisciplinary Digital Publishing Institute (MDPI)

Sensors
2025 #25 Issue 13

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.

control , EMG signal processing , machine learning , prosthetic materials , robotic hand prosthesis , tactile sensing

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Science and Innovation Center “Artificial Intelligence”, Astana IT University, Astana, 010000, Kazakhstan
ReLive Research, Astana, 010000, Kazakhstan
Department of Information Technology and Entrepreneurship, Narva College, University of Tartu, Narva, 20307, Estonia
Institute of Automation and Information Technologies, Satbayev University, Almaty, 050000, Kazakhstan
Department of Orthopedics, Mother and Child Health Center, Corporate Fund University Medical Center, Astana, 010000, Kazakhstan
Faculty of Engineering and Mathematics, Hochschule Bielefeld, Bielefeld, 33619, Germany

Science and Innovation Center “Artificial Intelligence”
ReLive Research
Department of Information Technology and Entrepreneurship
Institute of Automation and Information Technologies
Department of Orthopedics
Faculty of Engineering and Mathematics

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