Design of expert active knn classifier algorithm using flow stroop colour word test to assess flow state

• Published in: Journal of intelligent & fuzzy systems 2022-01, Vol.42 (6), p.6131-6144
• Main Authors: Lingaraj, Vanitha, Kaliannan, Kalaiselvi, Rohini, Venmathi Asirvatham, Thevasigamani, Rajesh Kumar, Chinnasamy, Karthikeyan, Durairaj, Vijendra Babu, Periasamy, Keerthika
• Format: Article
• Language: English
• Subjects: Algorithms; Classifiers; Crime; Datasets; Electrocardiography; K-nearest neighbors algorithm; Machine learning; Physiology
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-212504

Flow state assessment is essential to understand the involvement of an individual in a particular task assigned. If there is no involvement in the task assigned then the individual in due course of time gets affected either by psychological or physiological illnesses. The National Crime Records Bureau (NCRB) statistics show that non-involvement in the task drive the individual to a depression state and subsequently attempt for suicide. Therefore, it is essential to determine the decrease in flow level at an earlier stage and take remedial steps to recover them. There are many invasive methods to determine the flow state, which is not preferred and the commonly used non-invasive method is the questionnaire and interview method, which is the subjective and retroactive method, and hence chance to fake the result is more. Hence, the main objective of our work is to design an efficient flow level measurement system that measures flow in an objective method and also determines real-time flow classification. The accuracy of classification is achieved by designing an Expert Active k-Nearest Neighbour (EAkNN) which can classify the individual flow state towards the task assigned into nine states using non-invasive physiological Electrocardiogram (ECG) signals. The ECG parameters are obtained during the performance of FSCWT. Thus this work is a combination of psychological theory, physiological signals and machine learning concepts. The classifier is designed with a modified voting rule instead of the default majority voting rule, in which the contribution probability of nearest points to new data is considered. The dataset is divided into two sets, training dataset 75%and testing dataset 25%. The classifier is trained and tested with the dataset and the classification efficiency is 95%.