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Sensors 2019, 19(2), 429; https://doi.org/10.3390/s19020429

A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
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Received: 10 December 2018 / Revised: 6 January 2019 / Accepted: 18 January 2019 / Published: 21 January 2019
(This article belongs to the Section Biosensors)
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Abstract

Existing research on stress recognition focuses on the extraction of physiological features and uses a classifier that is based on global optimization. There are still challenges relating to the differences in individual physiological signals for stress recognition, including dispersed distribution and sample imbalance. In this work, we proposed a framework for real-time stress recognition using peripheral physiological signals, which aimed to reduce the errors caused by individual differences and to improve the regressive performance of stress recognition. The proposed framework was presented as a transductive model based on transductive learning, which considered local learning as a virtue of the neighborhood knowledge of training examples. The degree of dispersion of the continuous labels in the y space was also one of the influencing factors of the transductive model. For prediction, we selected the epsilon-support vector regression (e-SVR) to construct the transductive model. The non-linear real-time features were extracted using a combination of wavelet packet decomposition and bi-spectrum analysis. The performance of the proposed approach was evaluated using the DEAP dataset and Stroop training. The results indicated the effectiveness of the transductive model, which had a better prediction performance compared to traditional methods. Furthermore, the real-time interactive experiment was conducted in field studies to explore the usability of the proposed framework. View Full-Text
Keywords: stress recognition; peripheral physiological signals; neighborhood knowledge; transductive SVR; learning scenario stress recognition; peripheral physiological signals; neighborhood knowledge; transductive SVR; learning scenario
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Li, M.; Xie, L.; Wang, Z. A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals. Sensors 2019, 19, 429.

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