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Sensors 2018, 18(11), 3691; https://doi.org/10.3390/s18113691

Hybrid System for Engagement Recognition During Cognitive Tasks Using a CFS + KNN Algorithm

1
Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka-Shi, Fukuoka 812-8582, Japan
2
Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka-Shi, Fukuoka 819-0395, Japan
*
Author to whom correspondence should be addressed.
Received: 7 September 2018 / Revised: 15 October 2018 / Accepted: 29 October 2018 / Published: 30 October 2018
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan 2018)
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Abstract

Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition. View Full-Text
Keywords: electroencephalography; electrocardiography; electrooculography; eye tracking; near-infrared spectroscopy; engagement recognition; KNN; CFS; hybrid system; sensor electroencephalography; electrocardiography; electrooculography; eye tracking; near-infrared spectroscopy; engagement recognition; KNN; CFS; hybrid system; sensor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zennifa, F.; Ageno, S.; Hatano, S.; Iramina, K. Hybrid System for Engagement Recognition During Cognitive Tasks Using a CFS + KNN Algorithm. Sensors 2018, 18, 3691.

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