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Stress Recognition from Heterogeneous Data

Abstract : In modern society, the stress of an individual has been found to be a common problem. Continuous stress can lead to various mental and physical problems and especially for the people who always face emergency situations (e.g., fireman): it may alter their actions and put them in danger. Therefore, it is meaningful to provide the assessment of the stress of an individual. Based on this idea, the Psypocket project is proposed which is aimed at making a portable system able to analyze accurately the stress state of an individual based on his physiological, psychological and behavioural modifications. It should then offer solutions for feedback to regulate this state.The research of this thesis is an essential part of the Psypocket project. In this thesis, we discuss the feasibility and the interest of stress recognition from heterogeneous data. Not only physiological signals, such as Electrocardiography (ECG), Electromyography (EMG) and Electrodermal activity (EDA), but also reaction time (RT) are adopted to recognize different stress states of an individual. For the stress recognition, we propose an approach based on a SVM classifier (Support Vector Machine). The results obtained show that the reaction time can be used to estimate the level of stress of an individual in addition or not to the physiological signals. Besides, we discuss the feasibility of an embedded system which would realize the complete data processing. Therefore, the study of this thesis can contribute to make a portable system to recognize the stress of an individual in real time by adopting heterogeneous data like physiological signals and RT
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Submitted on : Monday, March 19, 2018 - 12:46:07 PM
Last modification on : Friday, May 17, 2019 - 11:39:50 AM
Long-term archiving on: : Tuesday, September 11, 2018 - 8:46:40 AM


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  • HAL Id : tel-01737211, version 1



Bo Zhang. Stress Recognition from Heterogeneous Data. Human-Computer Interaction [cs.HC]. Université de Lorraine, 2017. English. ⟨NNT : 2017LORR0113⟩. ⟨tel-01737211⟩



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