Making a Case for Application of the Unsupervised PCA Algorithm for Simultaneous and Proportional Myoelectric Intention Estimation of Individual Fingers

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 12
• Main Authors: Jamal, Muhammad Zahak, Lee, Dong-Hyun, Hyun, Dong Jin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Covariance matrices; Electromyography; Embedding; End effectors; Estimation; Fingers; Hand (anatomy); Human motion; Intention estimation; Kernel; Kernels; multilayer nonlinear principal component analysis (ML-NLPCA); Multilayers; Muscles; Myoelectricity; nonlinear principal component analysis (NLPCA); Principal component analysis; principal component analysis (PCA); Principal components analysis; Proportional control; robotic hand; simultaneous and proportional control (SPC); surface electromyography (sEMG); Thumb; unsupervised learning
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2022.3152234

This article makes a case for the application of the conventional principal component analysis (PCA) unsupervised algorithm by modifying it with embedded high dimensional layers to achieve simultaneous and proportional intention estimation of individual fingers of the human body using surface electromyography (sEMG). Due to its linear nature, PCA in its original form is unfit for estimating intricate movements like individual fingers of the human hand. Hence, the PCA algorithm was improved using the application of nonlinear embedding of the original electromyography (EMG) data into a higher dimensional feature space. These embedding functions were used in single and multiple layers with their mapping functions evaluated from the PCA algorithm. The resulting algorithms from the application of these embedding functions are called nonlinear PCA (NLPCA) and multilayer NLPCA (ML-NLPCA). We compare these algorithms with the unsupervised algorithms of nonnegative matrix factorization (NMF), NMF with Hadamard product (NMF-HP), kernel NMF (kNMF), and kernel NMF-HP (kNMF-HP) from previous studies to draw comparisons in making our case. The models for each finger are trained blindly without any output values by the unsupervised algorithms to solve the individual finger estimation problem using an in- house eight-channel EMG acquisition system. It will be seen that ML-NLPCA produces the most effective results to reflect the movement of a human hand by generating simultaneous and proportional control (SPC) commands using a robot hand.