Smart Textiles and Artificial Intelligence for Analysis of Sleep Quality and Early Disease Diagnosis
Given the importance of sleep quality for preventing and treating a wide range of diseases, but also considering technological advances in artificial intelligence and smart textiles, the type and number of data recorded during sleep have increased considerably in recent years. Diseases such as Parki...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2023-09-01
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| Series: | Applied Medical Informatics |
| Subjects: | |
| Online Access: | https://ami.info.umfcluj.ro/index.php/AMI/article/view/939 |
| _version_ | 1797669498374848512 |
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| author | Alexandru-George BERCIU Eva DULF |
| author_facet | Alexandru-George BERCIU Eva DULF |
| author_sort | Alexandru-George BERCIU |
| collection | DOAJ |
| description | Given the importance of sleep quality for preventing and treating a wide range of diseases, but also considering technological advances in artificial intelligence and smart textiles, the type and number of data recorded during sleep have increased considerably in recent years. Diseases such as Parkinson's, cancer, scoliosis, diabetes, or heart disease can be diagnosed early with wearable sensors. Also, smart textiles, which involve integrating low-energy wearable sensors into clothing, allow analysis of sleep movements, patient position, and number of breaths per minute and can be used to automatically alert the doctor if the patient seems to need clinical care. However, current monitoring solutions are limited in the number of parameters considered to determine sleep quality, generally relying on empirical solutions to rate sleep or to identify a particular disease. To harness the amount of the recorded data and the advances of Artificial Intelligence, this paper proposes a fuzzy logic-based solution for the analysis and determination of a new sleep quality index for the early identification of diseases and reduction of associated risks. For the determination of this new index, the fuzzy system input was taken as values related to room temperature, skin temperature, environmental noise, blood pressure, pulse, VO2max and the number of breaths per minute a patient experiences during sleep. Using a predefined fuzzy rule table, each simulated sleep period was given a score in the range of 1-4, where 1 means totally inadequate sleep and 4 means excellent sleep quality. Recent findings were used to establish the range of variance of the 7 monitored parameters, as evaluated by scientific papers published in prestigious international journals. The system successfully passed the authors' tests and can be further improved by including additional parameters and by using a neural network to assess sleep quality using this new index. |
| first_indexed | 2024-03-11T20:45:16Z |
| format | Article |
| id | doaj.art-4ddd201312b245c98e26c7bf3432f351 |
| institution | Directory Open Access Journal |
| issn | 2067-7855 |
| language | English |
| last_indexed | 2024-03-11T20:45:16Z |
| publishDate | 2023-09-01 |
| publisher | Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca |
| record_format | Article |
| series | Applied Medical Informatics |
| spelling | doaj.art-4ddd201312b245c98e26c7bf3432f3512023-10-01T11:36:44ZengIuliu Hatieganu University of Medicine and Pharmacy, Cluj-NapocaApplied Medical Informatics2067-78552023-09-0145Suppl. S1S25S251046Smart Textiles and Artificial Intelligence for Analysis of Sleep Quality and Early Disease DiagnosisAlexandru-George BERCIU0Eva DULF1Automation Department, EnTReC, Technical University of Cluj-NapocaAutomation Department, EnTReC, Technical University of Cluj-NapocaGiven the importance of sleep quality for preventing and treating a wide range of diseases, but also considering technological advances in artificial intelligence and smart textiles, the type and number of data recorded during sleep have increased considerably in recent years. Diseases such as Parkinson's, cancer, scoliosis, diabetes, or heart disease can be diagnosed early with wearable sensors. Also, smart textiles, which involve integrating low-energy wearable sensors into clothing, allow analysis of sleep movements, patient position, and number of breaths per minute and can be used to automatically alert the doctor if the patient seems to need clinical care. However, current monitoring solutions are limited in the number of parameters considered to determine sleep quality, generally relying on empirical solutions to rate sleep or to identify a particular disease. To harness the amount of the recorded data and the advances of Artificial Intelligence, this paper proposes a fuzzy logic-based solution for the analysis and determination of a new sleep quality index for the early identification of diseases and reduction of associated risks. For the determination of this new index, the fuzzy system input was taken as values related to room temperature, skin temperature, environmental noise, blood pressure, pulse, VO2max and the number of breaths per minute a patient experiences during sleep. Using a predefined fuzzy rule table, each simulated sleep period was given a score in the range of 1-4, where 1 means totally inadequate sleep and 4 means excellent sleep quality. Recent findings were used to establish the range of variance of the 7 monitored parameters, as evaluated by scientific papers published in prestigious international journals. The system successfully passed the authors' tests and can be further improved by including additional parameters and by using a neural network to assess sleep quality using this new index.https://ami.info.umfcluj.ro/index.php/AMI/article/view/939smart textileswearable sensorsartificial intelligencefuzzy logic |
| spellingShingle | Alexandru-George BERCIU Eva DULF Smart Textiles and Artificial Intelligence for Analysis of Sleep Quality and Early Disease Diagnosis Applied Medical Informatics smart textiles wearable sensors artificial intelligence fuzzy logic |
| title | Smart Textiles and Artificial Intelligence for Analysis of Sleep Quality and Early Disease Diagnosis |
| title_full | Smart Textiles and Artificial Intelligence for Analysis of Sleep Quality and Early Disease Diagnosis |
| title_fullStr | Smart Textiles and Artificial Intelligence for Analysis of Sleep Quality and Early Disease Diagnosis |
| title_full_unstemmed | Smart Textiles and Artificial Intelligence for Analysis of Sleep Quality and Early Disease Diagnosis |
| title_short | Smart Textiles and Artificial Intelligence for Analysis of Sleep Quality and Early Disease Diagnosis |
| title_sort | smart textiles and artificial intelligence for analysis of sleep quality and early disease diagnosis |
| topic | smart textiles wearable sensors artificial intelligence fuzzy logic |
| url | https://ami.info.umfcluj.ro/index.php/AMI/article/view/939 |
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