Veer Karan, Vig Renu
Electronics and Communication Engineering Department, Panjab University, Chandigarh, India.
Biomed Tech (Berl). 2018 Mar 28;63(2):191-196. doi: 10.1515/bmt-2016-0224.
This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.
本文描述了主成分分析（PCA）在上肢信号分类中的效用。主成分分析是一种用于分析高维数据的强大工具。在此，探索了两种不同的输入策略。第一种方法使用基于上臂双位置的肌电信号采集，另一种则仅使用主成分分析来对表面肌电图（SEMG）信号进行分类。在七名受试者中测量了来自肱二头肌和肱三头肌的表面肌电图数据以及上臂的四种独立肌肉活动（总数据集 = 56）。用于分析的数据集通过特定类别的主成分矩阵进行旋转，以便在特征提取之前使测量数据去相关。
