Ni-HGr: A sEMG dataset for hand grasp recognition in non-ideal conditions

This study aims to create a robust hand grasp recognition system using surface electromyography (sEMG) data collected from four electrodes. The grasps to be utilized in this study include cylindrical grasp, spherical grasp, tripod grasp, lateral grasp, hook grasp, and pinch grasp. The proposed system seeks to address common challenges, such as electrode shift, inter-day difference, and individual difference, which have historically hindered the practicality and accuracy of sEMG-based systems. By addressing these issues, the researchers intend to develop a more reliable and user-friendly interface for applications in prosthetic devices, rehabilitation systems, and human-computer interaction. The challenge of electrode shift refers to the slight movement or repositioning of electrodes on the skin, which can occur due to natural muscle contractions or external factors, leading to variations in the sEMG signals. Similarly, inter-day differences arise from variations across different recording days. Also, individual difference which demonstrate variability between individuals through factors like body mass, thickness of surface muscles, etc. These factors contribute to inconsistencies in the sEMG signals, making it challenging to achieve stable and repeatable gesture recognition. Furthermore, creating a system that is independent of specific electrode locations on the arm enhances the device's adaptability and ease of use, allowing for greater flexibility in real-world applications. This project will employ advanced signal processing and machine learning techniques to mitigate the effects of these variables, thereby ensuring high accuracy and reliability in hand grasp recognition regardless of the electrode position, day of recording or individual difference. The ultimate goal is to develop a versatile sEMG-based control system that can be seamlessly integrated into various practical applications, enhancing the quality of life for users.