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2 Biocybernetics and Biomedical Engineering

2 2013

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Surface electromyography for assessing triceps brachii muscle activities: a literature review

Ali A., Sundaraj K., Ahmad R. B., Ahamed N. U., Islam A.

Biocybernetics and Biomedical Engineering

2013

Vol. 33, no. 4

187--195

The goal of this review was to summarise the scientific findings of research conducted on the triceps brachii muscle using surface electromyography. To achieve this goal, we searched through several articles available from the online databases ScienceDirect and SpringerLink published in the English language between January 2008 and June 2012. We specifically searched for the phrases ‘‘EMG’’ and ‘‘triceps brachii’’ in the title, abstract, keywords or methods sections. From a total of 569 articles we identified 77 potentially relevant studies where 42 studies have been examined triceps brachii muscle activity using surface electromyography that applied in the field of rehabilitation, physiological exercise, sports, and prosthesis control. Among the 42 articles found, 16 studies have been examined triceps brachii muscle activity in rehabilitation, 13 for physiological exercise, 9 for sports, and 4 for prosthesis control in this literature review. We therefore believe that the information contained in this review will greatly assist and guide the progress of studies that use surface electromyography to measure triceps brachii muscle activity in the context of rehabilitation, physiological exercise, sports, and prosthesis control.

Machine learning in lung sound analysis: a systematic review

Palaniappan R., Sundaraj K., Ahamed N. U.

Biocybernetics and Biomedical Engineering

2013

Vol. 33, no. 3

129--135

Machine learning has proven to be an effective technique in recent years and machine learning algorithms have been successfully used in a large number of applications. The development of computerized lung sound analysis has attracted many researchers in recent years, which has led to the implementation of machine learning algorithms for the diagnosis of lung sound. This paper highlights the importance of machine learning in computer-based lung sound analysis. Articles on computer-based lung sound analysis using machine learning techniques were identified through searches of electronic resources, such as the IEEE, Springer, Elsevier, PubMed and ACM digital library databases. A brief description of the types of lung sounds and their characteristics is provided. In this review, we examined specific lung sounds/disorders, the number of subjects, the signal processing and classification methods and the outcome of the analyses of lung sounds using machine learning methods that have been performed by previous researchers. A brief description on the previous works is thus included. In conclusion, the review provides recommendations for further improvements.