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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Siti Armiza Mohd Aris
Universiti Teknologi Malaysia (UTM)

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Stress classification based on human electromagnetic radiation analysis Tengku ‘Afiah Mardhiah Tengku Zainul Akmal; Abd Hafiz Qayyum Abd Talib; Siti Zura A. Jalil; Siti Armiza Mohd Aris
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp826-834

Abstract

Stress is a feeling of emotional or physical tension due to any events that makes one feel frustrated, angry or nervous. It a situation that trigger particular biological response when encounter a threat or challenge. This paper discussed stress classification based on human electromagnetic radiation (EMR). At first, the collected radiation frequency data is analyzed using multivariate analysis of variance (MANOVA) to identify the significance points for the classification. Then, the data is classified using locally weighted learning (LWL) algorithm. The results show stress classification using EMR based on third eye and throat chakra points obtained accuracy of more than 60%.
Segmentation of the human body based on frequency of human electromagnetic radiation Siti Zura A. Jalil; Siti Armiza Mohd Aris; Nurul Aini Bani; Mohd Nabil Muhtazaruddin; Sahnius Usman
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp268-275

Abstract

This paper discusses the body segment recognition based on human electromagnetic radiation frequency. Twenty-three points of human electromagnetic radiation are studied experimentally from thirty-three healthy human subjects. Three human body segments are considered, namely Left, Right and Chakra. For the purpose of recognition, k-Nearest Neighbor (KNN) algorithm is used to classify the segments of the human body. Then, the performances of classification are determined based on the accuracy and Receiving Operating Characteristic (ROC) analysis. It is found that the proposed technique accurately classifies the body segments with 100% accuracy, thus suggest that the proposed technique is significant to classify human body segments.
Co-Authors Abd Hafiz Qayyum Abd Talib Mohd Nabil Muhtazaruddin Nurul Aini Bani Sahnius Usman Siti Zura A. Jalil Tengku ‘Afiah Mardhiah Tengku Zainul Akmal