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Guo K, Lu J, Wu Y, Hu X, Yang H. The Latest Research Progress on Bionic Artificial Hands: A Systematic Review. MICROMACHINES 2024; 15:891. [PMID: 39064402 PMCID: PMC11278702 DOI: 10.3390/mi15070891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 07/01/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024]
Abstract
Bionic prosthetic hands hold the potential to replicate the functionality of human hands. The use of bionic limbs can assist amputees in performing everyday activities. This article systematically reviews the research progress on bionic prostheses, with a focus on control mechanisms, sensory feedback integration, and mechanical design innovations. It emphasizes the use of bioelectrical signals, such as electromyography (EMG), for prosthetic control and discusses the application of machine learning algorithms to enhance the accuracy of gesture recognition. Additionally, the paper explores advancements in sensory feedback technologies, including tactile, visual, and auditory modalities, which enhance user interaction by providing essential environmental feedback. The mechanical design of prosthetic hands is also examined, with particular attention to achieving a balance between dexterity, weight, and durability. Our contribution consists of compiling current research trends and identifying key areas for future development, including the enhancement of control system integration and improving the aesthetic and functional resemblance of prostheses to natural limbs. This work aims to inform and inspire ongoing research that seeks to refine the utility and accessibility of prosthetic hands for amputees, emphasizing user-centric innovations.
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Affiliation(s)
- Kai Guo
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Jingxin Lu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Yuwen Wu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Xuhui Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Hongbo Yang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
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Zandigohar M, Han M, Sharif M, Günay SY, Furmanek MP, Yarossi M, Bonato P, Onal C, Padır T, Erdoğmuş D, Schirner G. Multimodal fusion of EMG and vision for human grasp intent inference in prosthetic hand control. Front Robot AI 2024; 11:1312554. [PMID: 38476118 PMCID: PMC10927746 DOI: 10.3389/frobt.2024.1312554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/19/2024] [Indexed: 03/14/2024] Open
Abstract
Objective: For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. Current control methods based on physiological signals such as electromyography (EMG) are prone to yielding poor inference outcomes due to motion artifacts, muscle fatigue, and many more. Vision sensors are a major source of information about the environment state and can play a vital role in inferring feasible and intended gestures. However, visual evidence is also susceptible to its own artifacts, most often due to object occlusion, lighting changes, etc. Multimodal evidence fusion using physiological and vision sensor measurements is a natural approach due to the complementary strengths of these modalities. Methods: In this paper, we present a Bayesian evidence fusion framework for grasp intent inference using eye-view video, eye-gaze, and EMG from the forearm processed by neural network models. We analyze individual and fused performance as a function of time as the hand approaches the object to grasp it. For this purpose, we have also developed novel data processing and augmentation techniques to train neural network components. Results: Our results indicate that, on average, fusion improves the instantaneous upcoming grasp type classification accuracy while in the reaching phase by 13.66% and 14.8%, relative to EMG (81.64% non-fused) and visual evidence (80.5% non-fused) individually, resulting in an overall fusion accuracy of 95.3%. Conclusion: Our experimental data analyses demonstrate that EMG and visual evidence show complementary strengths, and as a consequence, fusion of multimodal evidence can outperform each individual evidence modality at any given time.
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Affiliation(s)
- Mehrshad Zandigohar
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Mo Han
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Mohammadreza Sharif
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Sezen Yağmur Günay
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Mariusz P. Furmanek
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, United States
- Institute of Sport Sciences, Academy of Physical Education in Katowice, Katowice, Poland
| | - Mathew Yarossi
- Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, United States
| | - Paolo Bonato
- Motion Analysis Lab, Spaulding Rehabilitation Hospital, Charlestown, MA, United States
| | - Cagdas Onal
- Soft Robotics Lab, Worcester Polytechnic Institute, Worcester, MA, United States
| | - Taşkın Padır
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Deniz Erdoğmuş
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Gunar Schirner
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
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Kim YH, Choi YR, Joo DJ, Baek WY, Suh YC, Oh WT, Cho JY, Lee SC, Kim SK, Ryu HJ, Jeon KO, Lee WJ, Hong JW. Reaching New Heights: A Comprehensive Study of Hand Transplantations in Korea after Institutionalization of Hand Transplantation Law. Yonsei Med J 2024; 65:108-119. [PMID: 38288651 PMCID: PMC10827641 DOI: 10.3349/ymj.2023.0365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/26/2023] [Accepted: 11/02/2023] [Indexed: 02/01/2024] Open
Abstract
PURPOSE With the revision of the Organ and Transplantation Act in 2018, the hand has become legal as an area of transplantable organs in Korea. In January 2021, the first hand allotransplantation since legalization was successfully performed, and we have performed a total of three successful hand transplantation since then. By comparing and incorporating our experiences, this study aimed to provide a comprehensive reconstructive solution for hand amputation in Korea. MATERIALS AND METHODS Recipients were selected through a structured preoperative evaluation, and hand transplantations were performed at the distal forearm level. Postoperatively, patients were treated with three-drug immunosuppressive regimen, and functional outcomes were monitored. RESULTS The hand transplantations were performed without intraoperative complications. All patients had partial skin necrosis and underwent additional surgical procedures in 2 months after transplantation. After additional operations, no further severe complications were observed. Also, patients developed acute rejection within 3 months of surgery, but all resolved within 2 weeks after steroid pulse therapy. Motor and sensory function improved dramatically, and patients were very satisfied with the appearance and function of their transplanted hands. CONCLUSION Hand transplantation is a viable reconstructive option, and patients have shown positive functional and psychological outcomes. Although this study has limitations, such as the small number of patients and short follow-up period, we should focus on continued recovery of hand function, and be careful not to develop side effects from immunosuppressive drugs. Through the present study, we will continue to strive for a bright future regarding hand transplantation in Korea.
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Affiliation(s)
- Yo Han Kim
- Department of Plastic & Reconstructive Surgery, Yonsei University College of Medicine, Seoul, Korea
- Institute for Human Tissue Restoration, Yonsei University College of Medicine, Seoul, Korea
| | - Yun Rak Choi
- Department of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea
- Organ Transplantation Center, Severance Hospital, Seoul, Korea
| | - Dong Jin Joo
- Organ Transplantation Center, Severance Hospital, Seoul, Korea
- Department of Surgery, Yonsei University College of Medicine, Seoul, Korea
- The Research Institute for Transplantation, Yonsei University College of Medicine, Seoul, Korea
| | - Woo Yeol Baek
- Department of Plastic & Reconstructive Surgery, Yonsei University College of Medicine, Seoul, Korea
- Institute for Human Tissue Restoration, Yonsei University College of Medicine, Seoul, Korea
| | - Young Chul Suh
- Department of Plastic & Reconstructive Surgery, Yonsei University College of Medicine, Seoul, Korea
- Institute for Human Tissue Restoration, Yonsei University College of Medicine, Seoul, Korea
| | - Won Taek Oh
- Department of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea
| | - Jae Yong Cho
- Department of Orthopaedic Surgery, Yonsei University College of Medicine, Seoul, Korea
| | - Sang Chul Lee
- Department of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Sang Kyum Kim
- Department of Diagnostic Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Hyang Joo Ryu
- Department of Diagnostic Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Kyung Ock Jeon
- Organ Transplantation Center, Severance Hospital, Seoul, Korea
| | - Won Jai Lee
- Department of Plastic & Reconstructive Surgery, Yonsei University College of Medicine, Seoul, Korea
- Institute for Human Tissue Restoration, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Won Hong
- Department of Plastic & Reconstructive Surgery, Yonsei University College of Medicine, Seoul, Korea
- Institute for Human Tissue Restoration, Yonsei University College of Medicine, Seoul, Korea
- Organ Transplantation Center, Severance Hospital, Seoul, Korea.
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Odeyemi J, Ogbeyemi A, Wong K, Zhang W. On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning. Bioengineering (Basel) 2024; 11:108. [PMID: 38391594 PMCID: PMC10886041 DOI: 10.3390/bioengineering11020108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/24/2024] Open
Abstract
Prosthetic technology has witnessed remarkable advancements, yet challenges persist in achieving autonomous grasping control while ensuring the user's experience is not compromised. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge and improve the autonomy of prosthetics, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that an object's physical characteristics can affect the agent's ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object-gripping capabilities.
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Affiliation(s)
- Jethro Odeyemi
- Advanced Engineering Design Laboratory, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Akinola Ogbeyemi
- Advanced Engineering Design Laboratory, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Kelvin Wong
- Advanced Engineering Design Laboratory, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Wenjun Zhang
- Advanced Engineering Design Laboratory, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
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Recent Synergies of Machine Learning and Neurorobotics: A Bibliometric and Visualized Analysis. Symmetry (Basel) 2022. [DOI: 10.3390/sym14112264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Over the past decade, neurorobotics-integrated machine learning has emerged as a new methodology to investigate and address related problems. The combined use of machine learning and neurorobotics allows us to solve problems and find explanatory models that would not be possible with traditional techniques, which are basic within the principles of symmetry. Hence, neuro-robotics has become a new research field. Accordingly, this study aimed to classify existing publications on neurorobotics via content analysis and knowledge mapping. The study also aimed to effectively understand the development trend of neurorobotics-integrated machine learning. Based on data collected from the Web of Science, 46 references were obtained, and bibliometric data from 2013 to 2021 were analyzed to identify the most productive countries, universities, authors, journals, and prolific publications in neurorobotics. CiteSpace was used to visualize the analysis based on co-citations, bibliographic coupling, and co-occurrence. The study also used keyword network analysis to discuss the current status of research in this field and determine the primary core topic network based on cluster analysis. Through the compilation and content analysis of specific bibliometric analyses, this study provides a specific explanation for the knowledge structure of the relevant subject area. Finally, the implications and future research context are discussed as references for future research.
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