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Detail
Journal Article
Cognitive Load Recognition Based on T-Test and SHAP from Wristband Sensors
- Authors
- Jeong-Kyun Kim, Kangbok Lee, Sang Gi Hong
- Issue Date
- 2023-06
- Citation
- HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES, v.13, pp.1-14
- ISSN
- 2192-1962
- Publisher
- KOREA INFORMATION PROCESSING SOC
- Language
- English
- Type
- Journal Article
- Abstract
- The understanding of cognitive load plays a key role in increasing the potential of human-centric systems. Recently, cognitive load detection has attracted the attention of researchers. Competitions are being held and relevant data are being released in this regard. Managing cognitive load through wearable devices in daily life contributes, amongst others, to industrial safety. Wearable bands require high prediction results with less data because of their limited battery and processing power. Therefore, by detecting important features that characterize cognitive load, we aimed to obtain a high load detection classification accuracy using few features. In total, we detected 179 features such as heart rate variabilities, descriptive statistical, and frequency features. Important features were detected using the independent t-test and SHapley additive exPlanation (SHAP). Furthermore, an accuracy of 70.3% was obtained with only ten important features using the LightGBM classifier. Heart rate variability and galvanic skin response were used as the important features. Additionally, the discrete wavelet transform was used as a more important frequency-domain feature than the discrete cosine transform. The proposed cognitive load detection method achieved higher accuracy with fewer features using a lighter classifier than those reported by existing CogLoad data studies.
- KSP Keywords
- Cognitive load recognition, Detection Method, Discrete cosine Transform, Discrete wavelet transform(DWT-EE), Frequency domain(FD), Frequency feature, Frequency-domain feature, Galvanic skin response(GSR), High load, Industrial safety, Key role
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC)
(CC BY NC)
