Real-Time Breathing Phase Detection Using Earbuds Microphone
Tracking breathing phases (inhale and exhale) outside the hospitals can offer significant health and wellness benefits to users. For example, the breathing phases can provide fine-grained breathing information for proper meditation or breathing exercises. While previous works use smartphones and smartwatches for tracking breathing phases, in this work, we use earbuds for breathing phase detection, which has the potential to be a better form factor for breathing exercises as it requires less user attention from the user. We propose a convolutional neural network-based algorithm for detecting breathing phases using the audio captured through the earbuds during guided breathing sessions. We conducted a user study with 30 participants in both lab and home environments to develop and evaluate our algorithm. Our algorithm can detect the breathing phases with 85% accuracy by taking only 500ms audio signal. Our work demonstrates the potential of using earbuds for tracking the breathing phases in real-time.
Author: Retiree, Tousif Ahmed, Mahbubur Rahman, Mohsin Ahmed, Ebrahim Nematihosseinabadi, Jilong Kuang, Alex Gao
Published: IEEE-EMBS International Conference on Biomedical and Health Informatics(BHI) and the Body Sensor Networks(BSN) Conferences (IEEE BHI & BSN)
Date: Sep 27, 2022