Browsing School of Mechatronic Engineering (Articles) by Author "murugappan@unimap.edu.my"
Now showing items 1-12 of 12
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Classification of driver drowsiness level using wireless EEG
Mousa Kadhim, Wali; Murugappan, Muthusamy, Dr.; R. Badlishah, Ahmad, Prof. Dr. (Przegląd Elektrotechniczny, 2013)In this work, wireless Electroencephalogram (EEG) signals are used to classify the driver drowsiness levels (neutral, drowsy, high drowsy and sleep stage1) based on Discrete Wavelet Packet Transform (DWPT). Two statistical ... -
Combining spatial filtering and wavelet transform for classifying human emotions using EEG Signals
Murugappan; Nagarajan, Ramachandran, Prof. Dr.; Sazali, Yaacob, Prof. Dr. (Biomedical Engineering Society of the R.O.C., 2011)In this paper, we present human emotion assessment using electroencephalogram (EEG) signals. The combination of surface Laplacian (SL) filtering, time-frequency analysis of wavelet transform (WT) and linear classifiers are ... -
Detection of human stress using short-term ECG and HRV signals
Karthikeyan, Palanisamy; Murugappan, Muthusamy, Dr.; Sazali, Yaacob, Prof. Dr. (World Scientific Publishing Company, 2013-04)This paper introduces a method for resolving the problem of human stress detection through short-term (less than 5 min) electrocardiogram (ECG) and heart rate variability (HRV) signals. The explored methodology helps to ... -
Emotion recognition from facial EMG signals using higher order statistics and principal component analysis
Selvaraj, Jerritta; Murugappan, Muthusamy, Dr.; Wan Khairunizam, Wan Ahmad, Dr.; Sazali, Yaacob, Prof. Dr. (Taylor & Francis, 2014-04)Higher order statistics (HOS) is an efficient feature extraction method used in diverse applications such as bio signal processing, seismic data processing, image processing, sonar, and radar. In this work, we have ... -
Frequency band analysis of electrocardiogram (ECG) signals for human emotional state classification using discrete wavelet transform (DWT)
Murugappan, Muthusamy, Dr.; Murugappan, Subbulakshmi; Bong, Siao Zheng (Society of Physical Therapy Science, 2013-06)[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions ... -
Frequency study of facial electromyography signals with respect to emotion recognition
Selvaraj, Jerritta; Murugappan, Muthusamy, Dr.; Wan Khairunizam, Wan Ahmad, Dr.; Sazali, Yaacob, Prof. Dr. (Walter de Gruyter GmbH, 2014-01)Emotional intelligence is one of the key research areas in human-computer interaction. This paper reports the development of an emotion recognition system using facial electromyogram (EMG) signals focusing the ambiguity ... -
Hypovigilance detection using energy of electrocardiogram signals
Arun, S.; Sundaraj, Kenneth, Prof. Madya Dr.; Murugappan, M. (NISCAIR PUBLICATIONS, 2012-12)Driver drowsiness and driver inattention are the major causes for road accidents leading to severe traumas such as physical injuries, deaths, and economic losses. This necessitates the need for a system that can alert the ... -
Inter-hemispheric EEG coherence analysis in Parkinson's disease: Assessing brain activity during emotion processing
Yuvaraj, Rajamanickam; Murugappan, Muthusamy, Dr.; Norlinah, Mohamed Ibrahim, Dr.; Sundaraj, Kenneth, Prof. Dr.; Mohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr.; Khairiyah, Mohamad; Palaniappan, Ramaswamy; Satiyan, Marimuthu (Springer-Verlag Wien, 2014-06)Parkinson's disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the ... -
Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
Murukesan, L.; Murugappan, Muthusamy, Dr.; Muhammad Nadeem, Iqbal; Krishinan, Saravanan, Dr. (American Scientific Publishers, 2014-08)Sudden Cardiac Arrest (SCA) is a devastating heart abnormality which leads to millions of casualty per year. Thus, early detection or prediction of SCA could save the human lives in greater scale. This present work is aimed ... -
Methods and approaches on inferring human emotional stress changes through physiological signals: A review
Bong, Siao Zheng; Murugappan, Muthusamy, Dr.; Sazali, Yaacob, Prof. Dr. (Inderscience Enterprises Ltd, 2013)Emotional stress is kind of stressful state which is developed due to the continuous occurrence of negative emotions such as sad, disgust, angry and fear over a long period of time. In this work, a detailed investigation ... -
Multiple physiological signal-based human stress identification using non-linear classifiers
Karthikeyan, Palanisamy; Murugappan, Muthusamy, Dr.; Sazali, Yaacob, Prof. Dr. (2013)This paper describes the human stress identification using multiple physiological signals. The Electrocardiogram (ECG), Electromyogram (EMG), Heart Rate Variability (HRV), Galvanic Skin Response (GSR), and Skin Temperature ... -
PNN based driver drowsiness level classification using EEG
Mousa Kadhim, Wali; Murugappan, Muthusamy, Dr.; R. Badlishah, Ahmad, Prof. Dr. (JATIT & LLS. All rights reserved, 2013-06)In this work, we classify the driver drowsiness level (awake, drowsy, high drowsy and sleep stage1) based on different wavelets and probabilistic neural network classifier using wireless EEG signals. Deriving the amplitude ...