2020/08/16 by Ayesha Sadiq, Khan, Asad Mansoor, Sadiq, Ayesha +9 · 1 citation
Computer Science · Engineering · Neuroscience · #Advanced Sensor and Energy Harvesting Materials #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Gaze Tracking and Assistive Technology #Machine Learning (cs.LG) #Muscle activation and electromyography studies #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.06971
openalex publication_date 2020/08/16 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Daily life of thousands of individuals around the globe suffers due to physical or mental disability related to limb movement. The quality of life for such individuals can be made better by use of assistive applications and systems. In such scenario, mapping of physical actions from movement to a computer aided application can lead the way for solution. Surface Electromyography (sEMG) presents a non-invasive mechanism through which we can translate the physical movement to signals for classification and use in applications. In this paper, we propose a machine learning based framework for classification of 4 physical actions. The framework looks into the various features from different modalities which contribution from time domain, frequency domain, higher order statistics and inter channel statistics. Next, we conducted a comparative analysis of k-NN, SVM and ELM classifier using the feature set. Effect of different combinations of feature set has also been recorded. Finally, the classifier accuracy with SVM and 1-NN based classifier for a subset of features gives an accuracy of 95.21 and 95.83 respectively. Additionally, we have also proposed that dimensionality reduction by use of PCA leads to only a minor drop of less than 5.55% in accuracy while using only 9.22% of the original feature set. These finding are useful for algorithm designer to choose the best approach keeping in mind the resources available for execution of algorithm.