To avoid and manage stress-related problems, several works have investigated how to recognize stress states of people by exploiting the machine learning. Most of works utilize the supervised learning which requires labels for data to be trained and tested. Thus, labels which represent stress state of each data such as stressed or not become definitely important. Conventional stress state classification methods assign labels to unit data per a certain experimental period by applying stressor appearances or self-evaluation scores. However, those methods ignore temporal variations in stress responses which are involuntarily triggered inside the body within a shorter period of time than an experimental period. Therefore, we propose a stress state classification method by considering not only user's subjective evaluations but also temporal changes of stress responses in short periods. For the demonstration, we label our experimental data of 40 subjects by using our proposed classification method and conventional ones, respectively. Then, we train and test stress recognition models with 6 machine learning algorithms and our implemented neural network ones based on the labeled data. Finally, binary stress recognition with our proposed classification method improves the recognition accuracy by up to 31.6% as compared to those with conventional techniques.
|Title of host publication||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Editors||Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 2019 Nov|
|Event||2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States|
Duration: 2019 Nov 18 → 2019 Nov 21
|Name||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Conference||2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Period||19/11/18 → 19/11/21|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) [2016-0-00562(R0124-16-0002), Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly].
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Molecular Medicine
- Modelling and Simulation
- Health Informatics
- Pharmacology (medical)
- Public Health, Environmental and Occupational Health