Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors
Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep pos...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/458 |
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| author | Giacomo Peruzzi Alessandra Galli Giada Giorgi Alessandro Pozzebon |
| author_facet | Giacomo Peruzzi Alessandra Galli Giada Giorgi Alessandro Pozzebon |
| author_sort | Giacomo Peruzzi |
| collection | DOAJ |
| description | Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is essential in assessing and improving sleep quality. Automatic sleep posture detection systems, both wearable and non-wearable, have been developed to assess sleep quality. However, wearable solutions can be intrusive and affect sleep, while non-wearable systems, such as camera-based approaches and pressure sensor arrays, often face challenges related to privacy, cost, and computational complexity. The system in this paper proposes a microcontroller-based approach exploiting the execution of an embedded machine learning (ML) model for posture classification. By locally processing data from a minimal set of pressure sensors, the system avoids the need to transmit raw data to remote units, making it lightweight and suitable for real-time applications. Our results demonstrate that this approach maintains high classification accuracy (i.e., 0.90 and 0.96 for the configurations with 6 and 15 sensors, respectively) while reducing both hardware and computational requirements. |
| format | Article |
| id | doaj-art-1ffb088755ec4d978a5793eef7264261 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1ffb088755ec4d978a5793eef72642612025-01-24T13:49:00ZengMDPI AGSensors1424-82202025-01-0125245810.3390/s25020458Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure SensorsGiacomo Peruzzi0Alessandra Galli1Giada Giorgi2Alessandro Pozzebon3Department of Information Engineering, University of Padova, 35122 Padova, ItalyDepartment of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The NetherlandsDepartment of Information Engineering, University of Padova, 35122 Padova, ItalyDepartment of Information Engineering, University of Padova, 35122 Padova, ItalySleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is essential in assessing and improving sleep quality. Automatic sleep posture detection systems, both wearable and non-wearable, have been developed to assess sleep quality. However, wearable solutions can be intrusive and affect sleep, while non-wearable systems, such as camera-based approaches and pressure sensor arrays, often face challenges related to privacy, cost, and computational complexity. The system in this paper proposes a microcontroller-based approach exploiting the execution of an embedded machine learning (ML) model for posture classification. By locally processing data from a minimal set of pressure sensors, the system avoids the need to transmit raw data to remote units, making it lightweight and suitable for real-time applications. Our results demonstrate that this approach maintains high classification accuracy (i.e., 0.90 and 0.96 for the configurations with 6 and 15 sensors, respectively) while reducing both hardware and computational requirements.https://www.mdpi.com/1424-8220/25/2/458artificial intelligencesupport vector machinepressure sensorsembedded machine learninginternet of thingssensor selection |
| spellingShingle | Giacomo Peruzzi Alessandra Galli Giada Giorgi Alessandro Pozzebon Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors Sensors artificial intelligence support vector machine pressure sensors embedded machine learning internet of things sensor selection |
| title | Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors |
| title_full | Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors |
| title_fullStr | Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors |
| title_full_unstemmed | Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors |
| title_short | Sleep Posture Detection via Embedded Machine Learning on a Reduced Set of Pressure Sensors |
| title_sort | sleep posture detection via embedded machine learning on a reduced set of pressure sensors |
| topic | artificial intelligence support vector machine pressure sensors embedded machine learning internet of things sensor selection |
| url | https://www.mdpi.com/1424-8220/25/2/458 |
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