Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare Applications
Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of inte...
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MDPI AG
2025-06-01
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| author | Minh Long Hoang Guido Matrella Dalila Giannetto Paolo Craparo Paolo Ciampolini |
| author_facet | Minh Long Hoang Guido Matrella Dalila Giannetto Paolo Craparo Paolo Ciampolini |
| author_sort | Minh Long Hoang |
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| description | Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of intelligent healthcare systems. This research presents a comparative analysis of sleep position recognition using two distinct approaches: image-based deep learning and accelerometer-based classification. There are five classes: prone, supine, right side, left side, and wake up. For the image-based method, the Visual Geometry Group 16 (VGG16) convolutional neural network was fine-tuned with data augmentation strategies including rotation, reflection, scaling, and translation to enhance model generalization. The image-based model achieved an overall accuracy of 93.49%, with perfect precision and recall for “right side” and “wakeup” positions, but slightly lower performance for “left side” and “supine” classes. In contrast, the accelerometer-based method employed a feedforward neural network trained on features extracted from segmented accelerometer data, such as signal sum, standard deviation, maximum, and spike count. This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. The results demonstrate that while image-based models are effective, accelerometer-based classification offers higher precision and robustness, particularly in real-time and privacy-sensitive scenarios. Further comparisons of the system characteristics, data size, and training time are also carried out to offer crucial insights for selecting the appropriate technology in clinical, in-home, or embedded healthcare monitoring applications. |
| format | Article |
| id | doaj-art-3648ba4bb80f4169a00edae66b2e3a0b |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-3648ba4bb80f4169a00edae66b2e3a0b2025-08-20T03:29:39ZengMDPI AGSensors1424-82202025-06-012512381610.3390/s25123816Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare ApplicationsMinh Long Hoang0Guido Matrella1Dalila Giannetto2Paolo Craparo3Paolo Ciampolini4Department of Engineering and Architecture, University of Parma, 43124 Parma, ItalyDepartment of Engineering and Architecture, University of Parma, 43124 Parma, ItalyDepartment of Engineering and Architecture, University of Parma, 43124 Parma, ItalyDepartment of Engineering and Architecture, University of Parma, 43124 Parma, ItalyDepartment of Engineering and Architecture, University of Parma, 43124 Parma, ItalySleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of intelligent healthcare systems. This research presents a comparative analysis of sleep position recognition using two distinct approaches: image-based deep learning and accelerometer-based classification. There are five classes: prone, supine, right side, left side, and wake up. For the image-based method, the Visual Geometry Group 16 (VGG16) convolutional neural network was fine-tuned with data augmentation strategies including rotation, reflection, scaling, and translation to enhance model generalization. The image-based model achieved an overall accuracy of 93.49%, with perfect precision and recall for “right side” and “wakeup” positions, but slightly lower performance for “left side” and “supine” classes. In contrast, the accelerometer-based method employed a feedforward neural network trained on features extracted from segmented accelerometer data, such as signal sum, standard deviation, maximum, and spike count. This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. The results demonstrate that while image-based models are effective, accelerometer-based classification offers higher precision and robustness, particularly in real-time and privacy-sensitive scenarios. Further comparisons of the system characteristics, data size, and training time are also carried out to offer crucial insights for selecting the appropriate technology in clinical, in-home, or embedded healthcare monitoring applications.https://www.mdpi.com/1424-8220/25/12/3816accelerometerdeep neural networkvision-based systemsleep posture classificationCNN |
| spellingShingle | Minh Long Hoang Guido Matrella Dalila Giannetto Paolo Craparo Paolo Ciampolini Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare Applications Sensors accelerometer deep neural network vision-based system sleep posture classification CNN |
| title | Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare Applications |
| title_full | Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare Applications |
| title_fullStr | Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare Applications |
| title_full_unstemmed | Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare Applications |
| title_short | Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare Applications |
| title_sort | benchmarking accelerometer and cnn based vision systems for sleep posture classification in healthcare applications |
| topic | accelerometer deep neural network vision-based system sleep posture classification CNN |
| url | https://www.mdpi.com/1424-8220/25/12/3816 |
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