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A physiological measures-based method for detecting inattention in drivers using machine learning approach

Sahayadhas A., Sundaraj K., Murugappan M., Palaniappan R.

Biocybernetics and Biomedical Engineering

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2015

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Vol. 35, no. 3

198--205

EN

Background: In recent years, as a result of the usage of electronic gadgets in vehicles, driver inattention has become one of the major causes of road accidents that lead to severe physical injuries, deaths and significant economic losses. Statistics ensure the need of a reliable driver inattention detection system that can alert the driver before a mishap happens. Methods: In this work, we aimed to develop a system that can detect inattention using electrocardiogram (ECG) and surface electromyogram (sEMG) signals. Cognitive and visual inattention was manipulated by asking the driver to respond to phone calls and short messaging services, respectively. A total of 15 male subjects participated in the data collection process. The subjects were asked to drive for two hours in a simulated environment at three different times of the day. ECG, sEMG and video were obtained throughout the experiment. The gathered physiological signals were preprocessed to remove noises and artefacts. The inattention features were extracted from the preprocessed signals using conventional statistical, higher-order statistical and higher-order spectral features. The features were classified using k-nearest neighbour analysis, linear discriminant analysis and quadratic discriminant analysis. Results: The bispectral features gave overall maximum accuracies of 98.12% and 90.97% for the ECG and EMG signals, respectively. Conclusion: We conclude that ECG and EMG signals can be explored further to develop a robust and reliable inattention detection system.

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Clinical and non-clinical initial assessment of facial nerve paralysis: A qualitative review

Samsudin W. S. W., Sundaraj K.

Biocybernetics and Biomedical Engineering

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2014

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Vol. 34, no. 2

71--78

EN

This paper illustrates a brief review of some clinical and non-clinical methods to evaluate the facial nerve function in facial paralysis cases. A rigorous search of online databases such as IEEE, Springer, Elsevier, ACM digital library, Wiley online library, and Pub Med was conducted from January, 2012 to August, 2013 to discover and examine previous works on the field of facial treatment and rehabilitation. A brief introduction of facial nerve paralysis is provided. We examined the type of facial disorders, the number of subjects, and methods used to evaluate the facial nerve function. Different keywords were used to acquire the studies based on the desired criteria. A total of 80 articles were identified and were analysed for inclusion in this search. A brief discussion of both types of methods is presented. In conclusion, the review provides recommendations for further improvements.

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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

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2013

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Vol. 33, no. 4

187--195

EN

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.

4

Machine learning in lung sound analysis: a systematic review

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

Biocybernetics and Biomedical Engineering

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2013

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Vol. 33, no. 3

129--135

EN

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.