Multi-position Training Improves Robustness of Pattern Recognition and Reduces Limb-Position Effect in Prosthetic Control.
作者信息
Beaulieu Robert J, Masters Matthew R, Betthauser Joseph, Smith Ryan J, Kaliki Rahul, Thakor Nitish V, Soares Alcimar B
机构信息
Whiting School of Engineering, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Infinite Biomedical Technologies, Baltimore, MD, USA.
出版信息
J Prosthet Orthot. 2017 Apr;29(2):54-62. doi: 10.1097/JPO.0000000000000121.
Abstract
摘要
相似文献
1
Multi-position Training Improves Robustness of Pattern Recognition and Reduces Limb-Position Effect in Prosthetic Control.多位置训练可提高模式识别的稳健性并减少假肢控制中的肢体位置效应。
J Prosthet Orthot. 2017 Apr;29(2):54-62. doi: 10.1097/JPO.0000000000000121.
2
A characterization of the effect of limb position on EMG features to guide the development of effective prosthetic control schemes.肢体位置对肌电图特征影响的表征，以指导有效假肢控制方案的开发。
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:662-7. doi: 10.1109/EMBC.2014.6943678.
3
Resolving the limb position effect in myoelectric pattern recognition.解决肌电模式识别中的肢体位置效应。
IEEE Trans Neural Syst Rehabil Eng. 2011 Dec;19(6):644-51. doi: 10.1109/TNSRE.2011.2163529. Epub 2011 Aug 15.
4
Examining the adverse effects of limb position on pattern recognition based myoelectric control.研究肢体位置对基于模式识别的肌电控制的不良影响。
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:6337-40. doi: 10.1109/IEMBS.2010.5627638.
5
Improving myoelectric pattern recognition positional robustness using advanced training protocols.使用先进训练方案提高肌电模式识别的位置鲁棒性
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4828-31. doi: 10.1109/IEMBS.2011.6091196.
6
Towards better understanding and reducing the effect of limb position on myoelectric upper-limb prostheses.为了更好地理解并减少肢体位置对肌电上肢假肢的影响。
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:2577-80. doi: 10.1109/EMBC.2014.6944149.
7
Resolving the effect of wrist position on myoelectric pattern recognition control.解析手腕位置对肌电模式识别控制的影响。
J Neuroeng Rehabil. 2017 May 4;14(1):39. doi: 10.1186/s12984-017-0246-x.
8
Initial Clinical Evaluation of the Modular Prosthetic Limb.模块化假肢的初步临床评估。
Front Neurol. 2018 Mar 19;9:153. doi: 10.3389/fneur.2018.00153. eCollection 2018.
9
High-density force myography: A possible alternative for upper-limb prosthetic control.高密度肌动描记法：上肢假肢控制的一种可能替代方法。
J Rehabil Res Dev. 2016;53(4):443-56. doi: 10.1682/JRRD.2015.03.0041.
10
Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control.肌电图模式识别在上肢假肢控制中的评估：与直接肌电控制的对比案例研究。
J Neuroeng Rehabil. 2018 Mar 15;15(1):23. doi: 10.1186/s12984-018-0361-3.
引用本文的文献
1
A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.用于比较肌电假体控制器研究的多方面指标套件。
PLoS One. 2024 May 13;19(5):e0291279. doi: 10.1371/journal.pone.0291279. eCollection 2024.
2
Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review.用于上肢假肢控制的非侵入性肌电神经接口的生物机器人研究：十年回顾
Natl Sci Rev. 2023 Feb 24;10(5):nwad048. doi: 10.1093/nsr/nwad048. eCollection 2023 May.
3
Limb loading enhances skill transfer between augmented and physical reality tasks during limb loss rehabilitation.肢体负重增强了肢体缺失康复过程中增强现实和物理现实任务之间的技能转移。
J Neuroeng Rehabil. 2023 Jan 27;20(1):16. doi: 10.1186/s12984-023-01136-5.
4
An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition.一种自适应多模态控制策略，用于减轻肌电模式识别中的肢体位置效应。
Sensors (Basel). 2021 Nov 7;21(21):7404. doi: 10.3390/s21217404.
5
Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life.基于回归的手部假肢在日常生活中控制的纵向案例研究。
Front Neurosci. 2020 Jun 17;14:600. doi: 10.3389/fnins.2020.00600. eCollection 2020.
6
Understanding Limb Position and External Load Effects on Real-Time Pattern Recognition Control in Amputees.理解肢体位置和外部负载对截肢者实时模式识别控制的影响。
IEEE Trans Neural Syst Rehabil Eng. 2020 Jul;28(7):1605-1613. doi: 10.1109/TNSRE.2020.2991643. Epub 2020 May 11.
7
Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity.肌电控制的当前趋势和混杂因素：肢体位置和收缩强度。
Sensors (Basel). 2020 Mar 13;20(6):1613. doi: 10.3390/s20061613.
8
Cross-cultural adaptation and measurement properties of the French version of the Trinity Amputation and Prosthesis Experience Scales-Revised (TAPES-R).跨文化调适与三一体截肢与义肢体验量表修订版（TAPES-R）法译本的测量特性。
PLoS One. 2020 Feb 21;15(2):e0229084. doi: 10.1371/journal.pone.0229084. eCollection 2020.
9
Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning.基于极限学习的自适应稀疏表示的肌电假肢控制的肢体位置容差模式识别
IEEE Trans Biomed Eng. 2018 Apr;65(4):770-778. doi: 10.1109/TBME.2017.2719400. Epub 2017 Jun 23.
本文引用的文献
1
Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol.高密度肌电图信号的空间相关性为肌电控制的模式识别提供了对电极数量和偏移具有鲁棒性的特征。
IEEE Trans Neural Syst Rehabil Eng. 2015 Mar;23(2):189-98. doi: 10.1109/TNSRE.2014.2366752. Epub 2014 Nov 6.
2
Adaptive common average filtering for myocontrol applications.用于肌电控制应用的自适应公共平均滤波
Med Biol Eng Comput. 2015 Feb;53(2):179-86. doi: 10.1007/s11517-014-1215-1. Epub 2014 Nov 12.
3
A Multi-Class Proportional Myocontrol Algorithm for Upper Limb Prosthesis Control: Validation in Real-Life Scenarios on Amputees.一种用于上肢假肢控制的多类比例肌电控制算法：在截肢者现实生活场景中的验证
IEEE Trans Neural Syst Rehabil Eng. 2015 Sep;23(5):827-36. doi: 10.1109/TNSRE.2014.2361478. Epub 2014 Oct 3.
4
The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges.从表面肌电图中提取神经信息以控制上肢假肢：新出现的途径和挑战。
IEEE Trans Neural Syst Rehabil Eng. 2014 Jul;22(4):797-809. doi: 10.1109/TNSRE.2014.2305111. Epub 2014 Feb 11.
5
Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features.利用时变光谱特征实现肢体位置不变的肌电模式识别。
Neural Netw. 2014 Jul;55:42-58. doi: 10.1016/j.neunet.2014.03.010. Epub 2014 Mar 28.
6
Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control.用于上肢假肢控制的表面肌电信号自校正模式识别系统
IEEE Trans Biomed Eng. 2014 Apr;61(4):1167-76. doi: 10.1109/TBME.2013.2296274.
7
Confidence-based rejection for improved pattern recognition myoelectric control.基于置信度的拒绝策略提高模式识别肌电控制性能。
IEEE Trans Biomed Eng. 2013 Jun;60(6):1563-70. doi: 10.1109/TBME.2013.2238939. Epub 2013 Jan 10.
8
High-density surface EMG maps from upper-arm and forearm muscles.上肢和前臂肌肉的高密度表面肌电图图谱。
J Neuroeng Rehabil. 2012 Dec 10;9:85. doi: 10.1186/1743-0003-9-85.
9
Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees.为了减轻手臂位置对基于肌电图模式识别的桡骨截肢者运动分类的影响。
J Neuroeng Rehabil. 2012 Oct 5;9:74. doi: 10.1186/1743-0003-9-74.
10
Improving myoelectric pattern recognition positional robustness using advanced training protocols.使用先进训练方案提高肌电模式识别的位置鲁棒性
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4828-31. doi: 10.1109/IEMBS.2011.6091196.
Zotero 插件