Hoang Minh Long, Matrella Guido, Giannetto Dalila, Craparo Paolo, Ciampolini Paolo
Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy.
Sensors (Basel). 2025 Jun 18;25(12):3816. doi: 10.3390/s25123816.
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.
睡眠姿势识别在诊断和管理各种健康状况（如睡眠呼吸暂停、压疮和肌肉骨骼疾病）中起着至关重要的作用。睡眠期间对身体姿势的准确监测可以为临床医生提供有价值的见解，并支持智能医疗系统的发展。本研究对使用两种不同方法的睡眠姿势识别进行了比较分析：基于图像的深度学习和基于加速度计的分类。有五个类别：俯卧、仰卧、右侧卧、左侧卧和醒来。对于基于图像的方法，视觉几何组16（VGG16）卷积神经网络通过包括旋转、反射、缩放和平移在内的数据增强策略进行了微调，以提高模型的泛化能力。基于图像的模型总体准确率达到93.49%，对于“右侧卧”和“醒来”姿势的精确率和召回率完美，但对于“左侧卧”和“仰卧”类别性能略低。相比之下，基于加速度计的方法采用了前馈神经网络，该网络在从分段加速度计数据中提取的特征（如信号总和、标准差、最大值和峰值计数）上进行训练。该方法表现更优，在大多数睡眠姿势下准确率超过99.8%。由于人不在床上时没有心跳或呼吸等身体运动，“醒来”姿势特别容易检测。结果表明，虽然基于图像的模型有效，但基于加速度计的分类提供了更高的精度和鲁棒性，特别是在实时和对隐私敏感的场景中。还对系统特性、数据大小和训练时间进行了进一步比较，为在临床、家庭或嵌入式医疗监测应用中选择合适的技术提供关键见解。
