AI-Enabled Smart Hygiene System for Real-Time Glucose Detection
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918146459303936 |
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| author | Parvez, Khan Masood Rahaman, Sk Md Abidar Sichani, Ali Shiri AliAkbarpour, Hadi |
| author_facet | Parvez, Khan Masood Rahaman, Sk Md Abidar Sichani, Ali Shiri AliAkbarpour, Hadi |
| contents | This research presents a smart urinary health monitoring system incorporating a coplanar waveguide (CPW)-fed slot-loop antenna biosensor designed to analyse various urine samples. The antenna demonstrates distinct resonant frequency shifts when exposed to five specific urine conditions, deviating from its baseline 1.42 GHz operation. These measurable frequency variations enable the antenna to function as an effective microwave sensor for urinary biomarker detection. A potential artificial intelligence-based Convolutional Neural Networks Long Short-Term Memory (CNN-LSTM) framework is also discussed to overcome the limitations of overlapping frequency responses, aiming to improve the accuracy of health condition detection. These components contribute to the development of a smart toilet system that displays real-time health information on a wall-mounted urinal screen, without requiring any user effort or behavioural change. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19107 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Enabled Smart Hygiene System for Real-Time Glucose Detection Parvez, Khan Masood Rahaman, Sk Md Abidar Sichani, Ali Shiri AliAkbarpour, Hadi Systems and Control This research presents a smart urinary health monitoring system incorporating a coplanar waveguide (CPW)-fed slot-loop antenna biosensor designed to analyse various urine samples. The antenna demonstrates distinct resonant frequency shifts when exposed to five specific urine conditions, deviating from its baseline 1.42 GHz operation. These measurable frequency variations enable the antenna to function as an effective microwave sensor for urinary biomarker detection. A potential artificial intelligence-based Convolutional Neural Networks Long Short-Term Memory (CNN-LSTM) framework is also discussed to overcome the limitations of overlapping frequency responses, aiming to improve the accuracy of health condition detection. These components contribute to the development of a smart toilet system that displays real-time health information on a wall-mounted urinal screen, without requiring any user effort or behavioural change. |
| title | AI-Enabled Smart Hygiene System for Real-Time Glucose Detection |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.19107 |