Physiological Computing & AI

A 2021 report from World Intellectual Property Organization (WIPO) introduced new emerging Assistive Technologies (ATs): self-care technologies (eg health and emotion monitoring) and brain-computer interfaces for enhanced communication and mobility [1]. At the centre of the aforementioned ATs is artificial intelligence (AI)-powered physiological computing - a rapidly growing research area on enabling technologies that help us to listen to our bodily functions and psychophysiological needs and self-regulate [2-5]. The bodily functions are measured by physiological sensors, such as wearable heart rate monitor. AI plays a pivotal role in interpreting the physiological activities into the needs. Then, captured information is fed back into us to increase the awareness of our psychophysiological states and have greater control of our body and mind, promoting positivity gradually over time.

We are part of Global Disability Innovation Hub - WHO Collaborating Centre for Assistive Technology

Visit the lab webpage: https://youngjuncho.com/physiological-computing-artificial-intelligence/

Current Projects

• Remote Physiological Sensing: Thermal Imaging & remote PPG
• Social Biofeedback Interaction Framework
• Thermal Imaging
• Stress, Mental Workload, Task Difficulty
• Brain-Computer Interfaces (BCI) & computerised Cognitive Behavioural Therapy (cCBT)

Past Projects

• Comfort AI (Phase II: 2021-2022) 

References

[1] WIPO Technology Trends (2021). Assistive Technology, https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055_2021.pdf
[2] Physiological Computing and Artificial Intelligence Lab, https://youngjuncho.com/physiological-computing-artificial-intelligence/
[3] Cho, Youngjun. Rethinking eye-blink: assessing task difficulty through physiological representation of spontaneous blinking. Proceedings of the 2021 CHI conference on human factors in computing systems. 2021.
[4] Moge, Clara, Katherine Wang, and Youngjun Cho. Shared User Interfaces of Physiological Data: Systematic Review of Social Biofeedback Systems and Contexts in HCI. CHI Conference on Human Factors in Computing Systems. 2022. doi:10.1145/3491102.3517495
[5] Chen, S., Cho, Y., Yu, K., Ferrari, L. M., & Bremond, F. (2022). Recognizing the State of Emotion, Cognition and Action from Physiological and Behavioural Signals. Frontiers in Computer Science, 4:998416. doi:10.3389/fcomp.2022.998416.