i-Mask: An Intelligent Mask for Breath-Driven Activity Recognition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866918136015486976 |
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| author | Sinha, Ashutosh Kumar Patel, Ayush Dudhat, Mitul Anand, Pritam Mishra, Rahul |
| author_facet | Sinha, Ashutosh Kumar Patel, Ayush Dudhat, Mitul Anand, Pritam Mishra, Rahul |
| contents | The patterns of inhalation and exhalation contain important physiological signals that can be used to anticipate human behavior, health trends, and vital parameters. Human activity recognition (HAR) is fundamentally connected to these vital signs, providing deeper insights into well-being and enabling real-time health monitoring. This work presents i-Mask, a novel HAR approach that leverages exhaled breath patterns captured using a custom-developed mask equipped with integrated sensors. Data collected from volunteers wearing the mask undergoes noise filtering, time-series decomposition, and labeling to train predictive models. Our experimental results validate the effectiveness of the approach, achieving over 95\% accuracy and highlighting its potential in healthcare and fitness applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04544 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | i-Mask: An Intelligent Mask for Breath-Driven Activity Recognition Sinha, Ashutosh Kumar Patel, Ayush Dudhat, Mitul Anand, Pritam Mishra, Rahul Machine Learning Artificial Intelligence The patterns of inhalation and exhalation contain important physiological signals that can be used to anticipate human behavior, health trends, and vital parameters. Human activity recognition (HAR) is fundamentally connected to these vital signs, providing deeper insights into well-being and enabling real-time health monitoring. This work presents i-Mask, a novel HAR approach that leverages exhaled breath patterns captured using a custom-developed mask equipped with integrated sensors. Data collected from volunteers wearing the mask undergoes noise filtering, time-series decomposition, and labeling to train predictive models. Our experimental results validate the effectiveness of the approach, achieving over 95\% accuracy and highlighting its potential in healthcare and fitness applications. |
| title | i-Mask: An Intelligent Mask for Breath-Driven Activity Recognition |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.04544 |