IJBTT

A Methodical Approach for Comprehensive Analysis of Prosthetic Hand Designs

A Methodical Approach for Comprehensive Analysis of Prosthetic Hand Designs
	© 2025 by IJETT Journal
	Volume-73 Issue-4
	Year of Publication : 2025
	Author : Shripad Bhatlawande, Swati Shilaskar, Rushikesh Borse, Anjali Solanke, Jyoti Madake
	DOI : 10.14445/22315381/IJETT-V73I4P133

How to Cite?
Shripad Bhatlawande, Swati Shilaskar, Rushikesh Borse, Anjali Solanke, Jyoti Madake, "A Methodical Approach for Comprehensive Analysis of Prosthetic Hand Designs," International Journal of Engineering Trends and Technology, vol. 73, no. 4, pp.406-431, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I4P133

Abstract
This paper presents a methodical approach to comprehensively analysing prosthetic hand designs over the past 20 years. The survey begins by examining prosthetic arm designs, highlighting the shift from basic mechanical structures to more sophisticated myoelectric systems, which were seen in around 55% of the systems, neuroprosthetic systems, which were preferred by 18% of researchers and other systems like EMG, Eye controlled systems and such which constitute 27%. Design approaches for improving the properties of the arm, such as the weight and utility of prosthetic arms, are discussed along with the integration of sensory feedback mechanisms. Functional aspects of prosthetic arms are explored in depth, including gripper and control capabilities. The survey covers the types of amputations, broadly Upper Limb Prostheses. Details regarding the design used for a prosthetic arm, the actuators used in the industry, the control for the arm, materials used and torque analysis for each type of amputation are studied thoroughly. It provides a methodical approach and direction for further improvement in the design and addressing identified challenges. The paper comprehensively reviews occupational activities made possible with the use of well-designed assistive aids. Propelling the field forward and addressing its challenges are the key themes of this article, which examines promising emerging technologies and research efforts.

Keywords
Myoelectric systems, Neuroprosthetic systems, Prosthetic arm, Robotic hand design, Torque analysis.

References
[1] John-John Cabibihan, M. Khaleel Abubasha, and Nitish Thakor, “A Method for 3-D Printing Patient-Specific Prosthetic Arms with High Accuracy Shape and Size,” IEEE Access, vol. 6, pp. 25029-25039, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Masahiro Yoshikawa et al., “Finch: Prosthetic Arm with Three Opposing Fingers Controlled by a Muscle Bulge,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 377-386, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Peter J. Kyberd, and Adrian Poulton, “Use of Accelerometers in the Control of Practical Prosthetic Arms,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 10, pp. 1884-1891, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Jae Hyung Lee et al., “Quantification of Upper Limb Isometric Force Control Abilities for Evaluating Upper Limb Functions Among Prosthetic Users,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2559-2568, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Benjamin Stephens-Fripp, Rahim Mutlu, and Gursel Alici, “A Comparison of Recognition and Sensitivity in the Upper Arm and Lower Arm to Mechanotactile Stimulation,” IEEE Transactions on Medical Robotics and Bionics, vol. 2, no. 1, pp. 76-85, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Daniel De Barrie, Rebecca Margetts, and Khaled Goher, “SIMPA: Soft-Grasp Infant Myoelectric Prosthetic Arm,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 699-704, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Sandra Marquez-Figueroa, Yuriy S. Shmaliy, and Oscar G. Ibarra-Manzano, “Improving Myoelectric Prosthetic Arm Proportional Control Under MUAP Envelope Colored Noise,” IEEE Access, vol. 9, pp. 42845-42856, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Morten B. Kristoffersen et al., “Should Hands Be Restricted When Measuring Able-Bodied Participants to Evaluate Machine Learning Controlled Prosthetic Hands?,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 9, pp. 1977-1983, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Namho Kim, Seongseop Yun, and Dongjun Shin, “A Bioinspired Lightweight Wrist for High-DoF Robotic Prosthetic Arms,” IEEE/ASME Transactions on Mechatronics, vol. 24, no. 6, pp. 2674-2683, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Matthew S. Fifer et al., “Simultaneous Neural Control of Simple Reaching and Grasping with the Modular Prosthetic Limb Using Intracranial EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 3, pp. 695-705, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Qiushi Fu, Fangchi Shao, and Marco Santello, “Inter-Limb Transfer of Grasp Force Perception with Closed-Loop Hand Prosthesis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 5, pp. 927-936, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Rocco A. Romeo et al., “Method for Automatic Slippage Detection with Tactile Sensors Embedded in Prosthetic Hands,” IEEE Transactions on Medical Robotics and Bionics, vol. 3, no. 2, pp. 485-497, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Mathilde Legrand et al., “Closing the Loop Between Body Compensations and Upper Limb Prosthetic Movements: A Feasibility Study,” IEEE Transactions on Medical Robotics and Bionics, vol. 3, no. 1, pp. 230-240, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Tommaso Lenzi, James Lipsey, and Jonathon W. Sensinger, “The RIC Arm-A Small Anthropomorphic Transhumeral Prosthesis,” IEEE/ASME Transactions on Mechatronics, vol. 21, no. 6, pp. 2660-2671, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Nili E. Krausz et al., “Intent Prediction Based on Biomechanical Coordination of EMG and Vision-Filtered Gaze for End-Point Control of an Arm Prosthesis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 6, pp. 1471-1480, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Gunter Kanitz et al., “Compliant Prosthetic Wrists Entail More Natural Use Than Stiff Wrists During Reaching, not (Necessarily) During Manipulation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 7, pp. 1407-1413, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Hirakjyoti Basumatary, and Shyamanta M. Hazarika, “State of the Art in Bionic Hands,” IEEE Transactions on Human-Machine Systems, vol. 50, no. 2, pp. 116-130, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jibo Bai et al., “Tactile Perception Information Recognition of Prosthetic Hand Based on DNN-LSTM,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Xiaying Wang et al., “Leveraging Tactile Sensors for Low Latency Embedded Smart Hands for Prosthetic and Robotic Applications,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-14, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Digby Chappell et al., “Virtual Reality Pre-Prosthetic Hand Training with Physics Simulation and Robotic Force Interaction,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4550-4557, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Mun Hyeok Chang et al., “Anthropomorphic Prosthetic Hand Inspired by Efficient Swing Mechanics for Sports Activities,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 1196-1207, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Yunan He et al., “Development of Distributed Control System for Vision-Based Myoelectric Prosthetic Hand,” IEEE Access, vol. 7, pp. 54542-54549, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Neil M. Bajaj, Adam J. Spiers, and Aaron M. Dollar, “State of the Art in Artificial Wrists: A Review of Prosthetic and Robotic Wrist Design,” IEEE Transactions on Robotics, vol. 35, no. 1, pp. 261-277, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Tom Hetto, Stefan Schulz, and Markus Reischl, “Categorization and Evaluation Methods for Control Strategies of Bilateral Tasks in Arm Prosthetics,” IEEE Access, vol. 11, pp. 52501-52508, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Henrique Dantas et al., “Shared Prosthetic Control Based on Multiple Movement Intent Decoders,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 5, pp. 1547-1556, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Nasser A. Alshammary, Daniel A. Bennett, and Michael Goldfarb, “Synergistic Elbow Control for a Myoelectric Transhumeral Prosthesis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 2, pp. 468-476, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Ricardo Garcia-Rosas et al., “Task-Space Synergies for Reaching Using Upper-Limb Prostheses,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 12, pp. 2966-2977, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Maxim Karrenbach et al., “Improving Automatic Control of Upper-Limb Prosthesis Wrists Using Gaze-Centered Eye Tracking and Deep Learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 340-349, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Luis Ccorimanya et al., “A Personalized 3D-Printed Hand Prosthesis for Early Intervention in Children with Congenital Below-Elbow Deficiency: User-Centered Design Case Study,” IEEE Access, vol. 11, pp. 50235-50251, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Benjamin A. Kent, and Erik D. Engeberg, “Robotic Hand Acceleration Feedback to Synergistically Prevent Grasped Object Slip,” IEEE Transactions on Robotics, vol. 33, no. 2, pp. 492-499, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[31] John-John Cabibihan et al., “Illusory Sense of Human Touch from a Warm and Soft Artificial Hand,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 3, pp. 517-527, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Meike A. Wilke et al., “The Interaction Between Feedback Type and Learning in Routine Grasping with Myoelectric Prostheses,” IEEE Transactions on Haptics, vol. 13, no. 3, pp. 645-654, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Boxuan Zhong, He Huang, and Edgar Lobaton, “Reliable Vision-Based Grasping Target Recognition for Upper Limb Prostheses,” IEEE Transactions on Cybernetics, vol. 52, no. 3, pp. 1750-1762, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Sidharth Pancholi, and Amit M. Joshi, “Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application,” IEEE Sensors Journal, vol. 18, no. 8, pp. 3436-3443, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Sidharth Pancholi, Amit M. Joshi, and Deepak Joshi, “DLPR: Deep Learning-Based Enhanced Pattern Recognition Frame-Work for Improved Myoelectric Prosthesis Control,” IEEE Transactions on Medical Robotics and Bionics, vol. 4, no. 4, pp. 991-999, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Weichao Guo et al., “Toward Human-in-the-Loop Shared Control for Upper-Limb Prostheses: A Systematic Analysis of State-of-the-Art Technologies,” IEEE Transactions on Medical Robotics and Bionics, vol. 5, no. 3, pp. 563-579, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Alex K. Vaskov et al., “Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 2841-2857, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Tobias Pistohl et al., “Artificial Proprioceptive Feedback for Myoelectric Control,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 3, pp. 498-507, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Silvia Muceli, Ning Jiang, and Dario Farina, “Extracting Signals Robust to Electrode Number and Shift for Online Simultaneous and Proportional Myoelectric Control by Factorization Algorithms,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 3, pp. 623-633, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Strahinja Dosen et al., “Sensory Feedback in Prosthetics: A Standardized Test Bench for Closed-Loop Control,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 2, pp. 267-276, 2015. [CrossRef]
[Google Scholar] [Publisher Link]
[41] Francesco Clemente et al., “Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 12, pp. 1314-1322, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Kenneth R. Lyons, and Sanjay S. Joshi, “Upper Limb Prosthesis Control for High-Level Amputees via Myoelectric Recognition of Leg Gestures,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 5, pp. 1056-1066, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Janne M. Hahne et al., “On the Utility of Bioimpedance in the Context of Myoelectric Control,” IEEE Sensors Journal, vol. 21, no. 17, pp. 19505-19515, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Gunter Kanitz, Christian Cipriani, and Benoni B. Edin, “Classification of Transient Myoelectric Signals for the Control of Multi-Grasp Hand Prostheses,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 9, pp. 1756-1764, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Daniel Johansen et al., “Control of a Robotic Hand Using a Tongue Control System-A Prosthesis Application,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1368-1376, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Luis Vargas et al., “Evoked Tactile Feedback and Control Scheme on Functional Utility of Prosthetic Hand,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1308-1315, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Luis Vargas et al., “Resembled Tactile Feedback for Object Recognition Using a Prosthetic Hand,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10977-10984, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Marina M.-C. Vidovic et al., “Improving the Robustness of Myoelectric Pattern Recognition for Upper Limb Prostheses by Covariate Shift Adaptation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 9, pp. 961-970, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Jacqueline S. Hebert et al., “Novel Targeted Sensory Reinnervation Technique to Restore Functional Hand Sensation After Transhumeral Amputation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 4, pp. 765-773, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[50] Hansraj Bhamra et al., “A 24μW, Batteryless, Crystal-free, Multinode Synchronized SoC “Bionode” for Wireless Prosthesis Control,” IEEE Journal of Solid-State Circuits, vol. 50, no. 11, pp. 2714-2727, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[51] Lizhi Pan, Dustin L. Crouch, and He Huang, “Myoelectric Control Based on a Generic Musculoskeletal Model: Toward a Multi-User Neural-Machine Interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 7, pp. 1435-1442, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[52] Pan Xu et al., “A Study of Handgrip Force Prediction Scheme Based on Electrical Impedance Myography,” IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, vol. 7, no. 1, pp. 90-98, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[53] Tessy M. Thomas et al., “Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 2, pp. 293-303, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[54] Mathilde Legrand et al., “Simultaneous Control of 2DOF Upper-Limb Prosthesis with Body Compensations-Based Control: A Multiple Cases Study,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1745-1754, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[55] Melanie F. Simons et al., “B:Ionic Glove: A Soft Smart Wearable Sensory Feedback Device for Upper Limb Robotic Prostheses,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 3311-3316, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[56] Shuto Ogihara et al., “Multi Surface Electrodes Nerve Bundles Stimulation on the Wrist: Modified Location of Tactile Sensation on the Palm,” IEEE Access, vol. 11, pp. 13794-13809, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[57] P. Capsi-Morales et al., “Adaptable Poly-Articulated Bionic Hands EnhanceBoth Performance and User’s Perception in Bilateral Amputation: A Case Study,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2437-2445, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[58] Arash Ajoudani et al., “Exploring Teleimpedance and Tactile Feedback for Intuitive Control of the Pisa/IIT SoftHand,” IEEE Transactions on Haptics, vol. 7, no. 2, pp. 203-215, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[59] Alix Chadwell et al., “Evaluating Reachable Workspace and User Control Over Prehensor Aperture for a Body-Powered Prosthesis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 9, pp. 2005-2014, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[60] Ehsan Rahiminejad et al., “A Biomimetic Circuit for Electronic Skin with Application in Hand Prosthesis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2333-2344, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[61] Hao Zhou, and Gursel Alici, “Non-Invasive Human-Machine Interface (HMI) Systems with Hybrid On-Body Sensors for Controlling Upper-Limb Prosthesis: A Review,” IEEE Sensors Journal, vol. 22, no. 11, pp. 10292-10307, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[62] Michael Reilly, and Kimberly Kontson, “Computational Musculoskeletal Modeling of Compensatory Movements in the Upper Limb,” Journal of Biomechanics, vol. 108, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[63] Anestis Mablekos-Alexiou et al., “A Biomechatronics-Based EPP Topology for Upper-Limb Prosthesis Control: Modeling & Benchtop Prototype,” Biomedical Signal Processing and Control, vol. 73, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[64] Yihui Zhao et al., “An EMG-Driven Musculoskeletal Model for Estimation of Wrist Kinematics Using Mirrored Bilateral Movement,” Biomedical Signal Processing and Control, vol. 81, pp. 1-8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[65] Julia Starke et al., “Semi-Autonomous Control of Prosthetic Hands Based on Multimodal Sensing, Human Grasp Demonstration and User Intention,” Robotics and Autonomous Systems, vol. 154, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[66] Adriano Leccia et al., “Development and Testing of a Virtual Simulator for a Myoelectric Prosthesis Prototype-The PRISMA Hand II-To Improve Its Usability and Acceptability,” Engineering Applications of Artificial Intelligence, vol. 121, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[67] Ahmed Badawy, and Richard Alfred, “Myoelectric Prosthetic Hand with a Proprioceptive Feedback System,” Journal of King Saud University-Engineering Sciences, vol. 32, no. 6, pp. 388-395, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[68] Kazuo Kiguchi, and Hiroshi Sato, “Development of an Intelligent Artificial Arm,” IFAC Proceedings Volumes, vol. 40, no. 16, pp. 230-234, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[69] Yazan Ali Jarrah et al., “High-Density Surface Emg Signal Quality Enhancement Via Optimized Filtering Technique for Amputees’ Motion Intent Characterization Towards Intuitive Prostheses Control,” Biomedical Signal Processing and Control, vol. 74, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[70] Liwei Cheng et al., “Robotic Arm Control System Based on Brain-Muscle Mixed Signals,” Biomedical Signal Processing and Control, vol. 77, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[71] Ankita Harijee et al., “Comparison of Health State Utility Measures and Disabilities of Arm, Shoulder and Hand Questionnaire Scores in Bilateral Hand Amputees,” Journal of Plastic, Reconstructive & Aesthetic Surgerym, vol. 75, no. 3, pp. 980-990, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[72] Franziska Mally, Stefan Litzenberger, and Anton Sabo, “Kinematics of Elite Unilateral Below-Elbow Amputee Treadmill-Running-A Case Study,” Procedia Engineering, vol. 112, pp. 449-454, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[73] Kiran Setty, Theo van Niekerk, and Riaan Stopforth, “Design Considerations of the Touch Hand 4,” Procedia CIRP, vol. 91, pp. 494-502, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[74] Javier Andrés-Esperanza, “Affordable 3D-Printed Tendon Prosthetic Hands: Expectations and Benchmarking Questioned,” Engineering Science and Technology, an International Journal, vol. 31, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[75] Ivan I. Borisov et al., “Prototyping of EMG-Controlled Prosthetic Hand with Sensory System,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 16027-16031, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[76] Gabriel D. Eisenberg, Kyle G.H.M. Fyvie, and Abdul-Khaaliq Mohamed, “Real-Time Segmentation and Feature Extraction of Electromyography: Towards Control of a Prosthetic Hand,” IFAC-PapersOnLine, vol. 50, no. 2, pp. 151-156, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[77] Mary Doyle-Kent et al., “Cost Oriented Robotic Arm Optimised to Aid Independence,” IFAC-PapersOnLine, vol. 52, no. 25, pp. 125-130, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[78] Pia Mühlbauer et al., “Demonstrator of a Low-Cost Hand Prosthesis,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 15998-16003, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[79] Narek N. Unanyan, and Alexey A. Belov, “Low-Price Prosthetic Hand Controlled by EMG Signals,” IFAC-PapersOnLine, vol. 54, no. 13, pp. 299-304, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[80] Yadong Yan et al., “Design, Kinematic Modeling and Evaluation of a Novel Soft Prosthetic Hand with Abduction Joints,” Medicine in Novel Technology and Devices, vol. 15, pp. 1-9, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[81] Nadica Miljković, and Milica S. Isaković, “Effect of the sEMG Electrode (re) Placement and Feature Set Size on the Hand Movement Recognition,” Biomedical Signal Processing and Control, vol. 64, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[82] Muniandi Arunraj, Andy Srinivasan, and Sridhar P. Arjunan, “A Real-Time Capable Linear Time Classifier Scheme for Anticipated Hand Movements Recognition from Amputee Subjects using Surface EMG Signals,” Irbm, vol. 42, no. 4, pp. 277-293, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[83] Koushik Bakshi, M. Manjunatha, and C.S. Kumar, “Estimation of Continuous and Constraint-Free 3 DoF Wrist Movements from Surface Electromyogram Signal using Kernel Recursive Least Square Tracker,” Biomedical Signal Processing and Control, vol. 46, pp. 104-115, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[84] D.S.V. Bandara et al., “Development of a Multi-DoF Transhumeral Robotic Arm Prosthesis,” Medical Engineering & Physics, vol. 48, pp. 131-141, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[85] Olivier Barron et al., “Recurrent Neural Network for Electromyographic Gesture Recognition in Transhumeral Amputees,” Applied Soft Computing, vol. 96, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[86] I. Batzianoulis et al., “EMG-Based Decoding of Grasp Gestures in Reaching-To-Grasping Motions,” Robotics and Autonomous Systems, vol. 91, pp. 59-70, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[87] Mercy Paul Selvan et al., “Prosthetic Hand using EMG,” International Conference on Mathematical Sciences, Chennai, India, vol. 1770, no. 1, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[88] A. Cranny et al., “Thick-Film Force and Slip Sensors for a Prosthetic Hand,” Sensors and Actuators A: Physical, vol. 123-124, pp. 162-171, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[89] Dustin L. Crouch, and He Huang, “Lumped-Parameter Electromyogram-Driven Musculoskeletal Hand Model: A Potential Platform for Real-Time Prosthesis Control,” Journal of Biomechanics, vol. 49, no. 16, pp. 3901-3907, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[90] Fatemeh Davarinia, and Ali Maleki, “Automated Estimation of Clinical Parameters by Recurrence Quantification Analysis of Surface EMG for Agonist/Antagonist Muscles in Amputees,” Biomedical Signal Processing and Control, vol. 68, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[91] Adriana Dapena et al., “Trade-Off Analysis between Performance and Real-Time Usability in Emg-Based Human-Machine Interfaces,” SSRN, pp. 1-23, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[92] Naishi Feng et al., “A Fiber-Reinforced Human-Like Soft Robotic Manipulator Based on sEMG Force Estimation,” Engineering Applications of Artificial Intelligence, vol. 86, pp. 56-67, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[93] Guillaume Gaudet, Maxime Raison, and Sofiane Achiche, “Classification of Upper Limb Phantom Movements in Transhumeral Amputees using Electromyographic and Kinematic Features,” Engineering Applications of Artificial Intelligence, vol. 68, pp. 153-164, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[94] He Mao et al., “Simultaneous Estimation of Grip Force and Wrist Angles by Surface Electromyography and Acceleration Signals,” Biomedical Signal Processing and Control, vol. 79, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[95] Narek N. Unanyan, and Alexey A. Belov, “Anthropomorphic Arm Control System with Remote Gesture Tracking.” IFAC-PapersOnLine, vol. 54, no. 13, pp. 443-448, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[96] Noemi Gozzi et al., “XAI for Myo-Controlled Prosthesis: Explaining EMG Data for Hand Gesture Classification,” Knowledge-Based Systems, vol. 240, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[97] Mads Jochumsen, Asim Waris, and Ernest Nlandu Kamavuako, “The Effect of Arm Position on Classification of Hand Gestures with Intramuscular EMG,” Biomedical Signal Processing and Control, vol. 43, pp. 1-8, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[98] Gregory Lecrivain, “Using Reverse Engineering and Computational Fluid Dynamics to Investigate a Lower Arm Amputee Swimmer's Performance,” Journal of Biomechanics, vol. 41, no. 13, pp. 2855-2859, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[99] Gregory Lecrivain et al., “Effect of Body Roll Amplitude and Arm Rotation Speed on Propulsion of Arm Amputee Swimmers,” Journal of Biomechanics, vol. 43, no. 6, pp. 1111-1117, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[100] Giuseppe Lisi et al., “From the Classification of EMG Signals to the Development of a New Lower Arm Prosthesis,” IFAC Proceedings Volumes, vol. 44, no. 1, pp. 6493-6498, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[101] Jianwei Liu et al., “Quantification and Solutions of Arm Movements Effect on sEMG Pattern Recognition,” Biomedical Signal Processing and Control, vol. 13, pp. 189-197, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[102] Mata Amritanandamayi Devi, Ganesha Udupa, and Pramod Sreedharan, “A Novel Underactuated Multi-Fingered Soft Robotic Hand for Prosthetic Application,” Robotics and Autonomous Systems, vol. 100, pp. 267-277, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[103] Laura A. Miller et al., “Control of a Six Degree of Freedom Prosthetic Arm After Targeted Muscle Reinnervation Surgery,” Archives of Physical Medicine and Rehabilitation, vol. 89, no. 11, pp. 2057-2065, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[104] Lizhi Pan et al., “Continuous Estimation of Finger Joint Angles Under Different Static Wrist Motions from Surface EMG Signals,” Biomedical Signal Processing and Control, vol. 14, pp. 265-271, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[105] Lizhi Pan, and He (Helen) Huang, “A Robust Model-Based Neural-Machine Interface Across Different Loading Weights Applied at Distal Forearm,” Biomedical Signal Processing and Control, vol. 67, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[106] Alok Prakash et al., “Force Myography Controlled Multifunctional Hand Prosthesis for Upper-Limb Amputees,” Biomedical Signal Processing and Control, vol. 62, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[107] Alok Prakash et al., “An Affordable Transradial Prosthesis Based on Force Myography Sensor,” Sensors and Actuators A: Physical, vol. 325, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[108] F. Riillo et al., “Optimization of EMG-Based Hand Gesture Recognition: Supervised vs. Unsupervised Data Preprocessing on Healthy Subjects and Transradial Amputees,” Biomedical Signal Processing and Control, vol. 14, pp. 117-125, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[109] Bin Yang et al., “Control of Myoelectric Prosthetic Hand with a Novel Proximity-Tactile Sensor,” Science China Technological Sciences, vol. 65, no. 7, pp. 1513-1523, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[110] Manish Raj Aryal, and Sirjana Pun, “Additive Manufacturing of Prosthetic Hands: A Brief Overview,” International Journal on Interactive Design and Manufacturing, vol. 16, no. 3, pp. 1099-1112, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[111] Hae-June Park et al., “Grasping Time and Pose Selection for Robotic Prosthetic Hand Control Using Deep Learning Based Object Detection,” International Journal of Control, Automation and Systems, vol. 20, no. 10, pp. 3410-3417, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[112] Francisco Gilfran A. Milfont, and Luis A. Gómez-Malagón, “Design of a Lightweight Hydraulic Myoelectric Prosthetic Hand,” Research on Biomedical Engineering, vol. 37, no. 4, pp. 867-879, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[113] Nguiadem Clautilde, Raison Maxime, Achiche Sofiane, “Impact of the Choice of Upper Limb Prosthesis Mechanism on Kinematics and Dynamic Quality,” Medical Engineering & Physics, vol. 94, pp. 16-25, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[114] Narek N. Unanyan, and Alexey A. Belov, “Design of Upper Limb Prosthesis using Real-Time Motion Detection Method Based on EMG Signal Processing,” Biomedical Signal Processing and Control, vol. 70, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[115] Oluwarotimi Williams Samuel et al., “Pattern Recognition of Electromyography Signals Based on Novel Time Domain Features for Amputees' Limb Motion Classification,” Computers & Electrical Engineering, vol. 67, pp. 645-655, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[116] Nianfeng Wang, Kunyi Lao, and Xianmin Zhang, “Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand,” Journal of Bionic Engineering, vol. 14, no. 1, pp. 47-59, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[117] Asim Waris et al., “The Effect of Time on EMG Classification of Hand Motions in Able-Bodied and Transradial Amputees,” Journal of Electromyography and Kinesiology, vol. 40, pp. 72-80, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[118] Jingzhou Yang et al., “A Multi-Fingered Hand Prosthesis,” Mechanism and Machine Theory, vol. 39, no. 6, pp. 555-581, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[119] Dapeng Yang et al., “Simultaneous Estimation of 2-DOF Wrist Movements Based on Constrained Non-Negative Matrix Factorization and Hadamard Product,” Biomedical Signal Processing and Control, vol. 56, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[120] Yang Yu et al., “Multi-DoF Continuous Estimation for Wrist Torques using Stacked Autoencoder,” Biomedical Signal Processing and Control, vol. 57, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[121] Xu Zhuojun, Tian Yantao, and Li Yang, “sEMG Pattern Recognition of Muscle Force of Upper Arm for Intelligent Bionic Limb Control,” Journal of Bionic Engineering, vol. 12, no. 2, pp. 316-323, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[122] M. Saiful Bahari et al., “Design and Development of a Multifingered Prosthetic Hand,” International Journal of Social Robotics, vol. 4, no. S1, pp. 59-66, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[123] Kostiantyn Vonsevych et al., “Fingers Movements Control System Based on Artificial Neural Network Model,” Radioelectronics and Communications Systems, vol. 62, no. 1, pp. 23-33, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[124] Zhirui Zhao et al., “Prescribed Performance Model-Free Adaptive Terminal Sliding Mode Control for the Pneumatic Artificial Muscles Elbow Exoskeleton,” Journal of Mechanical Science and Technology, vol. 35, no. 7, pp. 3183-3197, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[125] Zhang, Yi, Hua Deng, and Guoliang Zhong. “Humanoid Design of Mechanical Fingers using a Motion Coupling and Shape-Adaptive Linkage Mechanism,” Journal of Bionic Engineering, vol. 15, no. 1, pp. 94-105, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[126] Xin-qing Wang et al., “Design and Control of a Coupling Mechanism-Based Prosthetic Hand,” Journal of Shanghai Jiaotong University (Science), vol. 15, no. 5, pp. 571-577, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[127] Mario Baggetta et al., “Design, Modeling, and Control of a Variable Stiffness Elbow Joint,” The International Journal of Advanced Manufacturing Technology, vol. 122, no. 11-12, pp. 4437-4451, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[128] Guo-hong Chai et al., “Review on Tactile Sensory Feedback of Prosthetic Hands for the Upper-Limb Amputees by Sensory Afferent Stimulation,” Journal of Shanghai Jiaotong University (Science), vol. 19, no. 5, pp. 587-591, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[129] Majid Aljalal et al., “Comprehensive Review on Brain-Controlled Mobile Robots and Robotic Arms Based on Electroencephalography Signals,” Intelligent Service Robotics, vol. 13, no. 4, pp. 539-563, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[130] Xiao Dong Zhan, Taehun Kang, and Hyouk Ryeol Choi. “An Approach for Pattern Recognition of Hand Activities Based on EEG and Fuzzy Neural Network,” Journal of Mechanical Science and Technology, vol. 19, no. 1, pp. 87-96, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[131] Zhen Gang Xiao, and Carlo Menon, “Performance of Forearm FMG and sEMG for Estimating Elbow, Forearm and Wrist Positions,” Journal of Bionic Engineering, vol. 14, no. 2, pp. 284-295, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[132] Benjamin A. Kent, John Lavery, and Erik D. Engeberg, “Anthropomorphic Control of a Dexterous Artificial Hand Via Task Dependent Temporally Synchronized Synergies,” Journal of Bionic Engineering, vol. 11, no. 2, pp. 236-248, 2014. [CrossRef] [Google Scholar] [Publisher Link] [133] Hend ElMohandes et al., “A Multi-Kalman Filter-Based Approach for Decoding Arm Kinematics from EMG Recordings,” BioMedical Engineering OnLine, vol. 21, no. 1, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[134] Ziming Chen et al., “A Robotic Prosthetic Hand for Computer Mouse Operations,” Advanced Intelligent Systems, vol. 2025, pp. 1-12, 2025.
[CrossRef] [Google Scholar] [Publisher Link]