Signal Acquisition and Time-Frequency Perspective of EMG Signal-based Systems and Applications

• Published in: Technical review - IETE Vol. 41; no. 4; pp. 466 - 485
• Main Authors: Sharma, Anil, Sharma, Ila, Kumar, Anil
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 03.07.2024
• Subjects: Biomedical engineering; Data acquisition; Electromyography; Feature extraction; Pattern classification; Prosthetics; Signal processing
• ISSN: 0256-4602; 0974-5971
• DOI: 10.1080/02564602.2023.2265897

The last few decades have emerged as a remarkable era for exploring and employing electromyography (EMG) signals and their attributes in various applications such as clinical assessment and rehabilitation engineering. An EMG signal-based system encapsulates different domains of signal acquisition and processing, statistical analysis, and control systems in a single framework. This survey attempts to highlight and distinguish the time- and frequency-based signal processing according to the applications of EMG signals. When EMG signals are used for clinical assessment, time-frequency analysis involves transforming the signals in different domains and extracting useful physiological information. On the other hand, the concept of time and frequency deals with extracting time, frequency, or time-frequency-based features when EMG signals are used for pattern recognition-based control applications such as robotics and augmented reality. It is often very difficult and confusing to distinguish and establish a clear understanding between these domains reported in various literature. Hence, this study first presents different signal acquisition systems and pre-processing techniques, followed by comprehending the concepts in time, frequency, and time-frequency-based approaches based on the applications. Next, the review of various post-processing techniques, different feature extraction routines, and a survey of different classifiers used in the pattern recognition step is done. The work concludes with a study of innovative applications of EMG signals reported in recent years, provides an overview of EMG signal-based limb prosthetics, and suggests a few futuristic research ideas.