Wireless Earphone-based Real-Time Monitoring of Breathing Exercises: A Deep Learning Approach
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912236692307968 |
|---|---|
| author | Wazir, Hassam Khan Waghoo, Zaid Kapila, Vikram |
| author_facet | Wazir, Hassam Khan Waghoo, Zaid Kapila, Vikram |
| contents | Several therapy routines require deep breathing exercises as a key component and patients undergoing such therapies must perform these exercises regularly. Assessing the outcome of a therapy and tailoring its course necessitates monitoring a patient's compliance with the therapy. While therapy compliance monitoring is routine in a clinical environment, it is challenging to do in an at-home setting. This is so because a home setting lacks access to specialized equipment and skilled professionals needed to effectively monitor the performance of a therapy routine by a patient. For some types of therapies, these challenges can be addressed with the use of consumer-grade hardware, such as earphones and smartphones, as practical solutions. To accurately monitor breathing exercises using wireless earphones, this paper proposes a framework that has the potential for assessing a patient's compliance with an at-home therapy. The proposed system performs real-time detection of breathing phases and channels with high accuracy by processing a $\mathbf{500}$ ms audio signal through two convolutional neural networks. The first network, called a channel classifier, distinguishes between nasal and oral breathing, and a pause. The second network, called a phase classifier, determines whether the audio segment is from inhalation or exhalation. According to $k$-fold cross-validation, the channel and phase classifiers achieved a maximum F1 score of $\mathbf{97.99\%}$ and $\mathbf{89.46\%}$, respectively. The results demonstrate the potential of using commodity earphones for real-time breathing channel and phase detection for breathing therapy compliance monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10310 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Wireless Earphone-based Real-Time Monitoring of Breathing Exercises: A Deep Learning Approach Wazir, Hassam Khan Waghoo, Zaid Kapila, Vikram Audio and Speech Processing Machine Learning Several therapy routines require deep breathing exercises as a key component and patients undergoing such therapies must perform these exercises regularly. Assessing the outcome of a therapy and tailoring its course necessitates monitoring a patient's compliance with the therapy. While therapy compliance monitoring is routine in a clinical environment, it is challenging to do in an at-home setting. This is so because a home setting lacks access to specialized equipment and skilled professionals needed to effectively monitor the performance of a therapy routine by a patient. For some types of therapies, these challenges can be addressed with the use of consumer-grade hardware, such as earphones and smartphones, as practical solutions. To accurately monitor breathing exercises using wireless earphones, this paper proposes a framework that has the potential for assessing a patient's compliance with an at-home therapy. The proposed system performs real-time detection of breathing phases and channels with high accuracy by processing a $\mathbf{500}$ ms audio signal through two convolutional neural networks. The first network, called a channel classifier, distinguishes between nasal and oral breathing, and a pause. The second network, called a phase classifier, determines whether the audio segment is from inhalation or exhalation. According to $k$-fold cross-validation, the channel and phase classifiers achieved a maximum F1 score of $\mathbf{97.99\%}$ and $\mathbf{89.46\%}$, respectively. The results demonstrate the potential of using commodity earphones for real-time breathing channel and phase detection for breathing therapy compliance monitoring. |
| title | Wireless Earphone-based Real-Time Monitoring of Breathing Exercises: A Deep Learning Approach |
| topic | Audio and Speech Processing Machine Learning |
| url | https://arxiv.org/abs/2404.10310 |