CAJ | 학술논문

　　体表肌电信号会随着外部或人体内部环境变化而发生改变，这种时变特征使得固定参数肌电模式分类器的分类精度会随着时间的延长而下降。为了获得具有稳定性能的肌电假肢控制系统，在肌电模式分类器中加入自适应机制是很有必要的。本文以传统线性判别分析(Linear Discriminant Analysis，LDA)为基础，尝试在肌电模式分类器中引入三种自适应方案，并探讨了这三种方案在肌电模式分类应用中的优缺点。初步研究表明：自增强线性判别分析(Self-enhancing LDA， SELDA)分类器和循环训练集线性判别(Cycle Substitution LDA，CSLDA)分类器都能够将识别准确率提升5%左右。其中，SELDA 是一种有效的自适应方案，而 CSLDA 可以得到更高的识别率提升和更好的稳定性，但是计算量较大，需要更大的代价。卡尔曼自适应线性判别(Kalman Adaptive LDA，KALDA)分类器单独使用效果不明显，需要进一步改进或结合其他方法使用。
　　체표기전신호회수착외부혹인체내부배경변화이발생개변，저충시변특정사득고정삼수기전모식분류기적분류정도회수착시간적연장이하강。위료획득구유은정성능적기전가지공제계통，재기전모식분류기중가입자괄응궤제시흔유필요적。본문이전통선성판별분석(Linear Discriminant Analysis，LDA)위기출，상시재기전모식분류기중인입삼충자괄응방안，병탐토료저삼충방안재기전모식분류응용중적우결점。초보연구표명：자증강선성판별분석(Self-enhancing LDA， SELDA)분류기화순배훈련집선성판별(Cycle Substitution LDA，CSLDA)분류기도능구장식별준학솔제승5%좌우。기중，SELDA 시일충유효적자괄응방안，이 CSLDA 가이득도경고적식별솔제승화경호적은정성，단시계산량교대，수요경대적대개。잡이만자괄응선성판별(Kalman Adaptive LDA，KALDA)분류기단독사용효과불명현，수요진일보개진혹결합기타방법사용。
When the surface electromyography (sEMG) signals change along with external or internal environment of the human body, general pattern classifiers will lead to a decrease of identification accuracy since they do not update their parameters adaptively. In order to adapt to the time-varying characteristics of sEMG signals, three kinds of adaptive algorithms for updating the parameters of a classifier during the use of artificial limb were introduced to improve the classification accuracy of time-variant sEMG signals. The pilot results of this study show that self-enhancing linear discriminant analysis is an effective solution and cycle substitution linear discriminant analysis presents the best performance but requires a large amount of calculations. The performance of the Kalman adaptive linear discriminant analysis is not prominent when it was used alone, and therefore it needs to be combined with other methods.