Enabling Adaptive Cardio-Respiratory Biofeedback Training on Ubiquitous Hand-Worn Devices

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Authors: Ruotong Yu, Xintong Wu, Lily Sheng, Zihao Dong, Yuntao Wang, Yuanchun Shi
Cited by: 1
References: 80

Abstract

We introduce an adaptive cardio-respiratory biofeedback system implemented on ubiquitous hand-worn devices such as smart watches and rings, enabling accessible and real-time physiological training outside clinical settings. Users place a hand on their abdomen to promote embodied awareness of breathing rhythms, while PPG and IMU sensors continuously capture cardio-respiratory signals. Unlike conventional open-loop biofeedback that delivers fixed breathing guidance irrespective of user response, our system employs a closed-loop adaptation: real-time physiological signals adjust breathing cues to optimize cardio-respiratory coupling, ensuring personalized training trajectories. This shift from static to adaptive guidance markedly improves user engagement and training efficacy. A user performance evaluation study further showed that adaptive biofeedback significantly boosts HRV, prolongs high-HRV states, and enhances user experience, demonstrating clear advantages over non-adaptive methods. Together, these findings position adaptive, hand-worn biofeedback as a promising approach for ubiquitous, user-centered mental health interventions.

Fields of Study & Subjects

Heart Rate Variability and Autonomic Control Non-Invasive Vital Sign Monitoring Emotion and Mood Recognition

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–18
Publication date
13 April 2026