Machine Learning-Assisted Codebook Design for MMSE Channel Estimation

Breathing rate is critical for the user’s respiratory health, stress, and fitness level. Unfortunately, breathing rates are hard to track outside the clinical context, requiring specialized devices. While the mobile and wearable device-based approach could be helpful, existing methods require heavy engagement from the user. Furthermore, they can be inaccurate in the presence of minute body motions and loud noises. This paper presents a robust multimodal approach to tracking the users breathing rate by using a signal-processing-based algorithm on motion sensors and a lightweight machine learning algorithm on acoustic sensors from the earbuds. A comprehensive user study with 30 participants shows that our system can calculate the breathing rate reasonably (Mean Absolute Error < 2 BPM) in controlled and uncontrolled settings with varying body motion and environmental noises. This work provides an essential direction toward developing continuous and passive breathing rate monitoring in the wild.