Kazi Newaj Faisal

KAZI NEWAJ FAISAL

Assistant Professor (Instructor Class B)

Electrical, Electronics and Communication Engineering (EECE) Dept.

Military Institute of Science and Technology (MIST), Bangladesh

Recent Updates

Review of Automated Grasp Recognition using sEMG

Surface electromyography-based automated grasp recognition has emerged as a vital technology in various fields, such as automatic control, human-machine interfaces, prosthetics, and virtual reality. A review paper analyzing the available state-of-the-art methodologies for sEMG-based automated grasp recognition, covering sensing modalities, datasets, acquisition systems, pre-processing techniques, feature extraction, and identification systems has been published recently. The review also includes a detailed chronological analysis and comparison of grasp recognition techniques, focusing on the number of subjects and types of grasps. Additionally, the paper highlights open research issues and potential future prospects for these challenges, as well as several industry domains that can incorporate sEMG-based automated grasp recognition systems. [Details...]

Coronary Artery Disease Identification Using ECG

Coronary Artery Disease is a critical factor in various severe heart conditions. Early detection and treatment of CAD are crucial to prevent further disease progression. A novel approach utilizing improved energy estimation of electrocardiogram signals has been developed to automate the characterization of CAD conditions. The proposed method employs variational mode decomposition to overcome the challenges posed by the multi-component nature and nonlinear characteristics of ECG signals, enabling accurate energy estimation of ECG beats. Multiple statistical features extracted from the localized ECG beat regions were effectively classified with over 99.80% accuracy using an optimized ensemble classifier and 10-fold cross-validation. The findings demonstrate the effectiveness of the proposed methodology compared to contemporary approaches on identical datasets. [Details...]

EEG-based Brain-Computer Interface Development

Classifying Upper Limb Motor Imagery Tasks

Motor imagery research in brain-computer interfaces is critical for limb movement control. Also, cognitive robotics and engineering aim to develop brain-robot interfaces that enable effective collaboration between robots and humans. Advancements in neuroscience, robotics, and machine learning are expected to expand the scope of BRI applications, improving human-robot cooperation. Multiple approaches utilizing ratio of band power-based features, time-domain and band-power features to emphasize the inter-related information within EEG signals, combined with an optimized KNN and ensemble classifier, have been developed achieving better accuracy in categorizing MI tasks. [Details...]