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- W4308086475 abstract "Tracking breathing phases (inhale and exhale) outside the hospitals can offer significant health and wellness benefits. For example, the breathing phases can provide fine-grained breathing information for breathing exercises. While previous works use smartphones and smartwatches for tracking breathing phases, in this work, we use earbuds for breathing phase detection, which can 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 a 500ms audio signal. Our work demonstrates the potential of using earbuds for tracking the breathing phases in real-time." @default.
- W4308086475 created "2022-11-08" @default.
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- W4308086475 date "2022-09-27" @default.
- W4308086475 modified "2023-09-28" @default.
- W4308086475 title "Real-Time Breathing Phase Detection Using Earbuds Microphone" @default.
- W4308086475 doi "https://doi.org/10.1109/bsn56160.2022.9928520" @default.
- W4308086475 hasPublicationYear "2022" @default.
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