Tousif Ahmed
Samsung Research America, Inc., Mountain View, California, United States
Breathing rate is critical for the user's respiratory health and is hard to track outside the clinical context, requiring specialized devices. Earables could provide a convenient solution to track the breathing rate anywhere by leveraging the user's breathing-related motion and sound captured through the earables' motion sensors and microphones. However, small non-breathing head movements or background noises during the assessment affect the estimation accuracy. While noise filtering improves accuracy, it can discard valid measurements. This paper presents a multimodal approach to tracking the user's breathing rate using a signal-processing-based algorithm on motion sensors and a lightweight machine-learning algorithm on acoustic sensors from the earables that balances the accuracy and data retention. A user study with 30 participants shows that the system can accurately calculate breathing rate (Mean Absolute Error < 2 breaths per minute) while retaining most breathing sessions (75\%) performed in real-world settings. This work provides an essential direction for remote breathing rate monitoring.
https://doi.org/10.1145/3544548.3581265
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)