Robust stress classifier using adaptive neuro-fuzzy classifier-linguistic hedges


Mand, Ali Afzalian and Wen, Justine Seow Jia and Sayeed, Md. Shohel and Sim, Kok Swee (2017) Robust stress classifier using adaptive neuro-fuzzy classifier-linguistic hedges. In: 2017 International Conference on Robotics, Automation and Sciences (ICORAS), 27-29 Nov. 2017, Melaka, Malaysia.

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Recent studies show that chronic stress exposure can induce a long list of diseases that are prevalent in human body. In this paper, researchers work on measuring and analyzing stress level using human biosignal, electrocardiogram (ECG). First, a few preprocessing steps and different analysis domains is done onto the raw data signals to clean and extract any and every relevant features found in ECG signal. A Linguistic Hedges concept on fuzzy feature selection method is then proposed to select unique patterns from the listed heart rate variability features. From the extracted list of features, a neurofuzzy classifier (ANFC-LH) is used to classify the data points into 2 classes, high arousal and low arousal, high arousal indicating stress feature. Then a comparative study using different classification methods, including Multilayer Perceptron, kNearest Neighbor, and Linear Discriminant Analysis are used to determine the most relevant feature specifying high stress level. Comparing to MLP, kNN, and LDA, ANFC-LH achieved the highest recognition rate. This research paper also shows the effects of using dimension reduction methods on classification algorithms where the result of kNN and LDA improved about 20% when applied with dimension reduction method, however, MLP recognition rate deteriorates about 50% when classifying data point after dimension reduction.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Data mining
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 20 Apr 2021 13:26
Last Modified: 20 Apr 2021 13:26


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