WVD-MODWT: An Enhanced Wigner-Ville Distribution for the Automated Control of Human-Machine Interaction-based Systems

Kazi Newaj Faisal; Shivam Sharma; Rishi Raj Sharma

doi:10.1109/JAS.2026.126146

IEEE/CAA Journal of Automatica Sinica

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K. Faisal, S. Sharma, and R. Sharma, “WVD-MODWT: An enhanced wigner-ville distribution for the automated control of human-machine interaction-based systems,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126146

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K. Faisal, S. Sharma, and R. Sharma, “WVD-MODWT: An enhanced wigner-ville distribution for the automated control of human-machine interaction-based systems,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126146

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WVD-MODWT: An Enhanced Wigner-Ville Distribution for the Automated Control of Human-Machine Interaction-based Systems

doi: 10.1109/JAS.2026.126146

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Author Bio:
Kazi Newaj Faisal (Member, IEEE) received the B.Sc. and M.Sc. degrees in electrical and electronic engineering from Rajshahi University of Engineering and Technology, Bangladesh, and Ph.D. degree from Defence Institute of Advanced Technology, Pune, India in 2012, 2017 and 2025, respectively. Since 2015, he has been a faculty member of the Department of Electrical, Electronic, and Communication Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh and currently serving as an Associate Professor. Prior to joining MIST, he also worked as a faculty member at the Varendra University, Bangladesh. His research interests include time-frequency analysis, non-stationary signal processing, radar/ biomedical/ seismic signal processing, human-machine/computer interaction, pattern recognition, artificial intelligence and machine learning. He has published more than 25 publications. He has been honored with the Best Male Student Paper Award at the 2nd PCEMS (2023), and the Best Paper Award at the 4th IEEE SPICSCON (2025). He has also successfully completed several funded research and consultancy projects from the Govt. of Bangladesh and industries. He is member of IEEE, IEB, IEEE signal processing society, and serving as faculty adviser of IEEE SPS MIST SB chapter. He has served as session chair, organizing and technical member of several IEEE conferences and actively contributing as reviewer for multiple IEEE and other journals

Shivam Sharma is currently pursuing Ph.D. degree from the Defence Institute of Advanced Technology, India. He received his M.Tech. degree from the Defence Institute of Advanced Technology, India in signal processing and communication in 2021 and B.Tech. degree from Rajiv Gandhi Govt. Engg. College (RGGEC), Nagrota Bagwan, India in electronics and communication engineering (ECE) in 2018. His area of research includes signal processing, machine learning, human-computer interaction,and biomedical signals like EEG, ECoG, EMG, etc

Rishi Raj Sharma (Member, IEEE) received the B.Tech. degree in electronics and communication engineering from U.P. Technical University, India, and the M.Tech. degree in advanced networks from ABV–Indian Institute of Information Technology and Management, India. He received the Ph.D. degree from the Indian Institute of Technology Indore, India, in signal processing and machine intelligence. He is currently serving as an Assistant Professor in the Department of Electronics Engineering at the Defence Institute of Advanced Technology (Deemed University), India. Prior to joining DIAT, he worked as an Assistant Professor at the Military College of Telecommunication Engineering (Indian Army), India, and as a Research Scientist at the National Brain Research Centre, India. He also served as an Assistant Professor at the Faculty of Engineering and Technology, R.B.S. College, India, for five years. His research interests include signal processing and machine intelligence with applications in brain–computer interfacing, human–machine interaction, electronic warfare, unmanned aerial vehicles (UAVs), automotive radar, and medical robotics. He has published more than 70 publications. He guided more than 40 M.Tech. students, one Ph.D. scholar, and currently guiding six Ph.D. scholars. He was in the team to develop a high-power microwave-based system ‘Atulya’ to disintegrate the novel Coronavirus (COVID19) which works on 2.45 GHz. He is a recipient of the IET Premium Award (2019) and IET Premium Award (2020) from the Institution of Engineering and Technology (UK). In addition, he has been honored with the IETE-S.K. Mitra Memorial Award (2025), the Best Male Student Paper Award at the 2nd PCEMS (2023), and the IES SYPA Travel Award (2025). He has also successfully completed several funded research and consultancy projects. He regularly conducts specialized training programs for executives from Bharat Electronics Limited (BEL), CISF, BDL, and other PSUs on “IR missile technology for target detection”, “Avionics & Electronic Warfare Systems”, “Modern Digital Communication Systems”, and “Counter-Unmanned Aerial Systems”. He is faculty mentor for “I4 Marine Technology Pvt. Ltd.”. He is Fellow of IETE, Member of IEEE and The Robotics Society. He is Associate Editor of IEEE Transactions on Instrumentation and Measurement
Corresponding author: Kazi Newaj Faisal, e-mail: newajfaisal@gmail.com
Received Date: 2025-05-30
Revised Date: 2026-03-16
Accepted Date: 2026-04-27

Available Online: 2026-11-10

Abstract

Abstract

With the progression of artificial intelligence, deep learning and machine learning, applications of human-machine interaction (HMI)-based systems are continually developing to facilitate more adaptable and context-aware interfaces. Incorporating diverse input and output signals enables HMI systems to offer a dynamic and interactive user experience, allowing automated control over various devices and processes. Wigner-Ville distribution (WVD) stands as a prominent quadratic TFR method capable of substantially contributing to the processing of non-stationary signals toward automated control of HMI-based systems. However, cross-term interference in WVD restricts its applicability and effectiveness. This paper presents a novel maximal overlap discrete wavelet transform (MODWT)-driven framework to attenuate cross-term interference within the WVD of non-stationary multi-component signals. The presented MODWT-based enhanced WVD (WVD-MODWT) method leverages the inherent characteristics of cross-terms components while combining the advantages of linear and quadratic TFR methods. Efficacy of the WVD-MODWT method is demonstrated on multiple synthetic signals, surpassing existing relevant techniques across various performance measures. The presented WVD-MODWT method is also applied to practical signals from two separate HMI-based systems: automated eye movement classification based on electromyogram signals and hand gesture recognition using accelerometer-based mechanomyogram data. Time-frequency-based features extracted from 2-D segmented TFRs generated by the WVD-MODWT method with an optimized ensemble classifier could achieve an overall accuracy of 99.17% and 97.06% for automated eye movement classification and automated hand gesture recognition, respectively. Comparative analysis with recent approaches on the same dataset and other relevant methods have revealed the supremacy of the proposed WVD-MODWT method in processing signals from HMI-based automated systems.

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