Current Solutions and Future Trends for Robotic Prosthetic Hands

被引:67
作者
Mendez, Vincent [1 ,2 ]
Iberite, Francesco [3 ,4 ]
Shokur, Solaiman [1 ,2 ]
Micera, Silvestro [1 ,2 ,3 ,4 ]
机构
[1] Ecole Polytech Fed Lausanne, Ctr Neuroprosthet, CH-1202 Geneva, Switzerland
[2] Ecole Polytech Fed Lausanne, Inst Bioengn, CH-1202 Geneva, Switzerland
[3] Scuola Super Sant Anna, BioRobot Inst, I-56127 Pisa, Italy
[4] Scuola Super Sant Anna, Dept Excellence Robot & AI, I-56127 Pisa, Italy
来源
ANNUAL REVIEW OF CONTROL, ROBOTICS, AND AUTONOMOUS SYSTEMS, VOL 4, 2021 | 2021年 / 4卷
基金
瑞士国家科学基金会;
关键词
hand; prosthesis; neuroprostheses; sensory feedback; electromyography; EMG; SENSORY FEEDBACK-SYSTEM; DEXTEROUS MANIPULATION; PATTERN-RECOGNITION; MYOELECTRIC CONTROL; ADULT NORMS; SURFACE EMG; LIMB; AMPUTEES; STIMULATION; ROBUST;
D O I
10.1146/annurev-control-071020-104336
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The desire for functional replacement of a missing hand is an ancient one. Historically, humans have replaced a missing limb with a prosthesis for cosmetic, vocational, or personal autonomy reasons. The hand is a powerful tool, and its loss causes severe physical and often mental debilitation. Technological advancements have allowed the development of increasingly effective artificial hands, which can improve the quality of life of people who suffered a hand amputation. Here, we review the state of the art of robotic prosthetic hands (RPHs), with particular attention to the potential and current limits of their main building blocks: the hand itself, approaches to decoding voluntary commands and controlling the hand, and systems and methods for providing sensory feedback to the user. We also briefly describe existing approaches to characterizing the performance of subjects using RPHs for grasping tasks and provide perspectives on the future of different components and the overall field of RPH development.
引用
收藏
页码:595 / 627
页数:33
相关论文
共 185 条
[1]  
Abayasiri RAM, 2020, 2020 6 INT C CONTR A
[2]   The JamHand: Dexterous Manipulation with Minimal Actuation [J].
Amend, John ;
Lipson, Hod .
SOFT ROBOTICS, 2017, 4 (01) :70-80
[3]   A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control [J].
Ameri, Ali ;
Akhaee, Mohammad Ali ;
Scheme, Erik ;
Englehart, Kevin .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2020, 28 (02) :370-379
[4]   Regression convolutional neural network for improved simultaneous EMG control [J].
Ameri, Ali ;
Akhaee, Mohammad Ali ;
Scheme, Erik ;
Englehart, Kevin .
JOURNAL OF NEURAL ENGINEERING, 2019, 16 (03)
[5]  
[Anonymous], 2002, P AUSTR C ROB AUT
[6]  
[Anonymous], 2018, ARXIV180301164CSCV
[7]   Artificial Redirection of Sensation From Prosthetic Fingers to the Phantom Hand Map on Transradial Amputees: Vibrotactile Versus Mechanotactile Sensory Feedback [J].
Antfolk, Christian ;
D'Alonzo, Marco ;
Controzzi, Marco ;
Lundborg, Goran ;
Rosen, Birgitta ;
Sebelius, Fredrik ;
Cipriani, Christian .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2013, 21 (01) :112-120
[8]  
Antfolk C, 2013, EXPERT REV MED DEVIC, V10, P45, DOI [10.1586/erd.12.68, 10.1586/ERD.12.68]
[9]   Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands [J].
Atzori, Manfredo ;
Cognolato, Matteo ;
Mueller, Henning .
FRONTIERS IN NEUROROBOTICS, 2016, 10
[10]   Comparative analysis of transverse intrafascicular multichannel, longitudinal intrafascicular and multipolar cuff electrodes for the selective stimulation of nerve fascicles [J].
Badia, Jordi ;
Boretius, Tim ;
Andreu, David ;
Azevedo-Coste, Christine ;
Stieglitz, Thomas ;
Navarro, Xavier .
JOURNAL OF NEURAL ENGINEERING, 2011, 8 (03)