Bernal, G., Hidalgo, N., Russomanno, C., & Maes, P. (2022, March). Galea: A physiological sensing system for behavioral research in Virtual Environments. In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 66-76). IEEE.
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Bernal, G., Hidalgo, N., Russomanno, C., & Maes, P. (2022, March). Galea: A physiological sensing system for behavioral research in Virtual Environments. In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 66-76). IEEE.
The pairing of Virtual Reality technology with Physiological Sensing has gained much interest in clinical settings and beyond: from developing novel methods for diagnosis of perception and cognition impairments, biofeedback for anxiety treatment, to enhancing everyday practices such as self-guided meditation. However, conducting this type of research does not come without challenges. For example, accessing the equipment for recording data from the user and synchronizing physiological response data with the stimuli or interactive environment are not trivial tasks, and generating virtual content in response to the user’s real-time data is costly and complex. This paper presents Galea, a device for multi-modal signal acquisition able to measure the physiological response of a user when experiencing virtual content, enabling behavioral, affective computing , and human-computer interaction research and applications to access data from the Parasympathetic nervous system and Sympathetic nervous system simultaneously. We present a primer on detectable human physiology as an input source for Physiological Computing from the perspective of the signals available through our device. We describe the primary design considerations and circuit characterization results of in-vivo recordings from the wearer’s brain, eyes, heart, skin, and muscles. We also present an example to help contextualize how these signals can be used in a virtual reality setting. Galea makes working with physiological sensors in virtual reality more accessible and can offer a standard for inter and intra experiment data comparisons. Lastly, we discuss the importance and contributions of this work as well as future challenges that need to be considered.