Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866916292467884032 |
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| author | Islam, Md Zobaer Martin, Brenden Gotcher, Carly Martinez, Tyler O'Hara, John F. Ekin, Sabit |
| author_facet | Islam, Md Zobaer Martin, Brenden Gotcher, Carly Martinez, Tyler O'Hara, John F. Ekin, Sabit |
| contents | In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous infrared light source and sensor. This light-wave sensing system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range with an average classification accuracy of up to 96.6% using machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_01367 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals Islam, Md Zobaer Martin, Brenden Gotcher, Carly Martinez, Tyler O'Hara, John F. Ekin, Sabit Signal Processing Machine Learning In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous infrared light source and sensor. This light-wave sensing system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range with an average classification accuracy of up to 96.6% using machine learning. |
| title | Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2311.01367 |