Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals
Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Furth...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/21/8568 |
| _version_ | 1797466488794251264 |
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| author | Lehel Dénes-Fazakas Máté Siket László Szilágyi Levente Kovács György Eigner |
| author_facet | Lehel Dénes-Fazakas Máté Siket László Szilágyi Levente Kovács György Eigner |
| author_sort | Lehel Dénes-Fazakas |
| collection | DOAJ |
| description | Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate—the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity. |
| first_indexed | 2024-03-09T18:39:34Z |
| format | Article |
| id | doaj.art-d451f2b9769e417e9b874d122dfde1a4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:39:34Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d451f2b9769e417e9b874d122dfde1a42023-11-24T06:50:06ZengMDPI AGSensors1424-82202022-11-012221856810.3390/s22218568Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate SignalsLehel Dénes-Fazakas0Máté Siket1László Szilágyi2Levente Kovács3György Eigner4Physiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, HungaryPhysiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, HungaryPhysiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, HungaryPhysiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, HungaryPhysiological Controls Research Center, Óbuda University, Bécsi út 96/b, H-1034 Budapest, HungaryNon-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate—the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity.https://www.mdpi.com/1424-8220/22/21/8568diabetes mellitusmachine learningphysical activity detection |
| spellingShingle | Lehel Dénes-Fazakas Máté Siket László Szilágyi Levente Kovács György Eigner Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals Sensors diabetes mellitus machine learning physical activity detection |
| title | Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals |
| title_full | Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals |
| title_fullStr | Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals |
| title_full_unstemmed | Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals |
| title_short | Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals |
| title_sort | detection of physical activity using machine learning methods based on continuous blood glucose monitoring and heart rate signals |
| topic | diabetes mellitus machine learning physical activity detection |
| url | https://www.mdpi.com/1424-8220/22/21/8568 |
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