An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition.
An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition.
复制标题
DOI：
10.3390/s17092020
复制
发表时间：
2017-09-04
期刊：
影响因子：
--
通讯作者：
Huang HH
中科院分区：
文献类型：
--
作者：
Liu M;Zhang F;Huang HH
基于表面肌电和机械传感器的运动模式识别（LMR）算法最近已经开发出来，并可用于动力假肢的神经控制。然而，在输入信号的变化，在传感器接口的物理变化和人体生理变化引起的，可能会威胁到这些算法的可靠性。本研究的目的是探讨应用自适应模式分类器的LMR的有效性。三个自适应分类器，即，基于熵的适应（EBA），学习从测试数据（LIFT），和Transductive支持向量机（TSVM），进行了比较和离线评估，使用从两个健全的主体和一个经股截肢者收集的数据。离线分析表明，自适应分类器可以有效地保持或恢复LMR算法的性能时，逐渐发生的信号变化。EBA和LIFT由于其更好的性能和更高的计算效率而被推荐。最后，EBA实现了实时人在回路假肢控制。在线评估表明，应用的EBA有效地适应了输入信号的变化，随着时间的推移，产生更可靠的假体控制相比，没有适应的LMR。所开发的新的自适应策略可以进一步提高神经控制假肢的可靠性。
Algorithms for locomotion mode recognition (LMR) based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA), LearnIng From Testing data (LIFT), and Transductive Support Vector Machine (TSVM), were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs.
登录
查看更多内容
一种用于长期肌电图 (EMG) 模式识别的新型无监督自适应学习方法
DOI：
10.3390/s17061370
发表时间：
2017-06-13
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Huang Q;Yang D;Jiang L;Zhang H;Liu H;Kotani K
通讯作者：
Kotani K
影响因子：
5.1
作者：
Hargrove, Levi;Englehart, Kevin;Hudgins, Bernard
通讯作者：
Hudgins, Bernard
影响因子：
158.5
作者：
Hargrove, Levi J.;Simon, Ann M.;Kuiken, Todd A.
通讯作者：
Kuiken, Todd A.
影响因子：
4.6
作者：
Ha, Kevin H.;Varol, Huseyin Atakan;Goldfarb, Michael
通讯作者：
Goldfarb, Michael
DOI：
10.1109/tgrs.2006.877950
发表时间：
2006-11-01
影响因子：
8.2
作者：
Bruzzone, Lorenzo;Chi, Mingmin;Marconcini, Mattia
通讯作者：
Marconcini, Mattia