Ramachandran, Nagarajan, Prof. Dr.
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/20370
2024-03-28T22:24:14ZHospital nurse following robot: hardware development and sensor integration
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/35295
Hospital nurse following robot: hardware development and sensor integration
Bukhari, Ilias; Ramachandran, Nagarajan, Prof. Dr.; Murugappan, M., Dr.; Khaled, Helmy, Dr.; Awang Sabri, Awang Omar; Muhammad Asyraf, Abdul Rahman
Hospital nurse regularly bring her instrument to the patient using cart. They need to push or pull the cart to the patient bed and bring it back many times in a day. This can be tiresome for nurses because they need to treat many patients in the hospital. This research is mainly to solve this problem by constructing a mobile robot for nurses that is able to follow and carry the medical equipment and at the same time perform obstacle avoidance. The designed robot has ability to move in and out at constricted space and is able to avoid any obstacles either static or dynamic. This robot can carry a load of 20 kg and used dc geared motor to move. The mobile platform is able to rotate at axial axis with the construction of special wheel and the placement of the motor. A suitable ultrasonic sensor bank is selected so that robot can detect obstacle around the mobile platform and avoid the obstacle. The robot control and obstacle avoidance system is designed by adopting the facilities of Basic ATOM microcontroller for better performance.
Link to publisher's homepage at http://www.inderscience.com
2014-01-01T00:00:00ZLifting scheme for human emotion recognition using EEG
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/34665
Lifting scheme for human emotion recognition using EEG
Murugappan, M., Dr.; Mohd Rizon, Mohamed Juhari; Nagarajan, Ramachandran, Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Ibrahim, Zunaidi; Hazry, Desa, Assoc. Prof. Dr.
In recent years, the need and importance of automatically recognizing emotions from EEG signals has grown with increasing role of brain computer interface applications. The detection of fine grained changes in functional state of human brain can be detected using EEG signals when compared to other physiological signals. This paper proposes an emotion recognition system from EEG (Electroencephalogram) signals. The audio-visual induction based acquisition protocol has been designed for acquiring the EEG signals under four emotions (disgust, happy, surprise and fear) for participants. Totally, 6 healthy subjects with an age group of 21–27 using 63 biosensors are used for registering the EEG signal for various emotions. After preprocessing the signals, two different lifting based wavelet transforms (LBWT) are employed to extract the three statistical features for classifying human emotions. In this work, we used Fuzzy C-Means (FCM) clustering for classifying the emotions. Results confirm the possibility of using two different lifting scheme based wavelet transform for assessing the human emotions from EEG signals.
Proceeding of The International Symposium on Information Technology 2008 (ITSim 2008) at Kuala Lumpur, Malaysia on 26 August 2008 through 29 August 2008. Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp
2008-08-01T00:00:00ZPatient monitoring in ICU under unstructured lighting condition
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/34384
Patient monitoring in ICU under unstructured lighting condition
Muhammad Naufal, Mansor; Sazali, Yaacob, Prof. Dr.; Nagarajan, Ramachandran, Prof. Dr.; Hariharan, Muthusamy, Dr.
In this paper, fuzzy classifier is explained and reviewed for detecting facial changes of patient in a hospital in Intensive Care Unit (ICU). The facial changes are most widely represented by eyes and mouth movements. The proposed system uses color images and it consists of three modules. The first module implements skin detection to detect the face. The second module constructs eye and mouth maps that are responsible for changes in eye and mouth regions. The third module extracts the features of eyes and mouth by processing the image and measuring certain dimensions of eyes and mouth regions. Finally a fuzzy classifier used to classify the movements at different illumination levels. From 300 samples of face images, it is found that the identification rate of awakness reaches 97%.
Proceeding of The Symposium on Industrial Electronics and Applications (ISIEA 2010) at Penang, Malaysia on 3 October 2010 through 5 October 2010.
Link to publisher's homepage at http://ezproxy.unimap.edu.my:2080/Xplore/dynhome.jsp
2010-10-01T00:00:00ZDetection of facial changes for ICU patients using KNN classifier
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/34367
Detection of facial changes for ICU patients using KNN classifier
Muhammad Naufal, Mansor; Sazali, Yaacob, Prof. Dr.; Nagarajan, Ramachandran; Che, Lim Sin; Hariharan, Muthusamy, Dr.; Muhd Ezanuddin, Abdul Aziz
This paper presents an integrated system for detecting facial changes of patient in a hospital in Intensive Care Unit(ICU).In this research we have considered the facial changes most widely represented by eyes and mouth movements. The proposed system uses color images and it consists of three modules.The first module implements skin detection to detect the face.The second module constructs eye and mouth maps that are responsible for changes in eye and mouth regions.The third module extracts the features of eyes and mouth by processing the image and measuring certain demensions of eyes and mouth regions. Finally the result of this work shows that the (k-NN) can be used for used to classify the awake ness with the average accuracy of 94%.
Proceeding of The International Conference on Intelligent and Advanced Systems 2010,(ICIAS 2010) at Kuala Lumpur, Malaysian from 15 June 2010 through 17 June 2010
2010-06-01T00:00:00Z