A Novel Time-Domain based Feature for EMG-PR Prosthetic and Rehabilitation Application

EMG signal is widely accepted in human-machine interaction applications, such as prosthesis control and rehabilitation devices. The existing feature extraction methods struggle to separate a variety of EMG based activities. In the proposed work, a novel feature defined as PAP (peak average power) has been proposed. This feature has been validated for NinaPro database which includes isometric, isotonic, grasp and finger force based upper limb motions. Further, the comparison of classification accuracy has been performed with well-known time domain based features. Significant classification performance enhancement has been observed in terms of accuracy with LDA and QDA techniques. In this experiment, three datasets have been created and analysis was performed. Consequently, the results show an average enhancement of 17.60%, 7.52% and 15.37% using the proposed approach for LDA in dataset-1, dataset-2, and dataset-3 respectively. Similarly for the same datasets, when QDA is used the proposed approach overrules the existing techniques with the average enhanced performance of 13.52%, 12.72%, and 15.40%. All the analysis has been done using MATLAB 2015a in the i7 core.

[1]  Amit M. Joshi,et al.  Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application , 2018, IEEE Sensors Journal.

[2]  Manfredo Atzori,et al.  Building the Ninapro database: A resource for the biorobotics community , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[3]  Manfredo Atzori,et al.  Electromyography data for non-invasive naturally-controlled robotic hand prostheses , 2014, Scientific Data.

[4]  Turan Dirlik,et al.  Application of computers in fatigue analysis , 1985 .

[5]  Ping Zhou,et al.  Wavelet Packet Feature Assessment for High-Density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation , 2016, Front. Neurol..

[6]  Sofiane Achiche,et al.  Classification of Upper limb phantom movements in transhumeral amputees using electromyographic and kinematic features , 2018, Eng. Appl. Artif. Intell..

[7]  Amit M. Joshi,et al.  Electromyography-Based Hand Gesture Recognition System for Upper Limb Amputees , 2019, IEEE Sensors Letters.

[8]  Loredana Zollo,et al.  Literature Review on Needs of Upper Limb Prosthesis Users , 2016, Front. Neurosci..

[9]  G. Naik,et al.  Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Amit M. Joshi,et al.  Time Derivative Moments Based Feature Extraction Approach for Recognition of Upper Limb Motions Using EMG , 2019, IEEE Sensors Letters.