A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

By Nurhazimah Nazmi, Mohd Azizi Abdul Rahman, Shin-ichiroh Yamamoto, Siti Anom Ahmad, Hairi Zamzuri, Saiful Amri Mazlan. Italian National Conference on Sensors, 2016

355 citations recorded by the source corpus.

In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3198ffd1c5f8a1db542fd00e87d4665d9da4fe49

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In Biomedical Engineering

In Cognitive Neuroscience

Citations in the corpus

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation (2019, background): “In addition, it does not require additional levels of data transformation [45].”