ABSTRACT
Existing stress measurement methods, including cortisol measurement, blood pressure monitoring, and psychometric testing, are invasive, impractical, or intermittent, limiting both clinical and biofeedback utility. Better stress measurement methods are needed for practical, widespread application. For the project ViRST, where we use a Virtual Reality (VR) environment controlled by a speech dialog system to provide chronic pain relief, we designed a novel stress biofeedback system. Our prototype employs an ear-clip Photoplethysmogram (PPG) sensor, an Arduino microcontroller, and a supervised learning algorithm. To acquire a training dataset, we ran stress induction experiments on 10 adult subjects aged 30-58 to track Heart Rate Variability (HRV) metrics and Discrete Wavelet Transform (DWT) coefficients. We trained an AdaBoost ensemble classifier to 93% 4-fold cross-validation accuracy and 93% precision. We outline future work to better suit a VR environment and facilitate additional modes of interaction by simplifying the human interface.
- John Allen. 2007. Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement 28, 3.Google ScholarCross Ref
- Anders la Cour-Harbo and Arne Jensen. 2014. Ripples in mathematics: The discrete wavelet transform. Springer.Google Scholar
- Inquisit 5 {Computer Software}. 2016. Retrieved from https://www.millisecond.com.Google Scholar
- Avinash Parnandi and Ricardo Gutierrez-Osuna. 2015. Physiological Modalities for Relaxation Skill Transfer in Biofeedback Games. In IEEE Journal of Biomedical and Health Information 21, 2.Google Scholar
- Nuno Pinheiro, Ricardo Couceiro, Jorge Henriques, Jens Muehlsteff, Iago Quintal, Lino Goncalves, and Paulo de Carvalho. 2016. Can PPG be used for HRV analysis? In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).Google ScholarCross Ref
- PulseSensor {Reflectance Photoplethysmogram}. World Famous Electronics Llc. Retrieved June 2, 2018 from https://pulsesensor.com.Google Scholar
- Jiri Pumprla, Kinga Howorka, David Groves, Michael Chester, and James Nolan. 2002. Functional assessment of heart rate variability: Physiological basis and practical applications. International Journal of Cardiology 84, 1, 1--14.Google ScholarCross Ref
- Fred Shaffer and J.P. Ginsberg. 2017. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health 5.Google Scholar
- Rodrigo Soares, Elton Siqueira, Marco Miura, Tiago Silva, and Carla Castanho. 2016. Biofeedback Sensors in Game Telemetry Research. In 2016 Proceedings of SBGames 15.Google Scholar
- Ahmad R. Subahni, Likun Xia, and Aamir S. Malik. 2012. Association of mental stress with video games. In Intelligent and Advanced Systems (ICIAS), 2012 4th International Conference 1, 82--85.Google Scholar
- Kavitha P. Thomas, A. P. Vinod, and Cuntai Guan. 2013. Enhancement of attention and cognitive skills using EEG based neurofeedback game. In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).Google ScholarCross Ref
- Tom N. Tombaugh. A comprehensive review of the Paced Auditory Serial Addition Test (PASAT). Archives of Clinical Neuropsychology 21, 1, 53--76.Google Scholar
- Constantine Tsigos and George P. Chrousos. (1995). Neuroendocrinology and Pathophysiology of the Stress System. Annals of the New York Academy of Sciences 771 (1 Stress), 1--18.Google Scholar
Index Terms
- Real-time stress assessment through PPG sensor for VR biofeedback
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