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Article

Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM)

1
Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
2
Research Institute of Smart Ageing, The Hong Kong Polytechnic University, Hong Kong 999077, China
*
Authors to whom correspondence should be addressed.
Sensors 2024, 24(15), 5016; https://doi.org/10.3390/s24155016
Submission received: 3 July 2024 / Revised: 1 August 2024 / Accepted: 1 August 2024 / Published: 2 August 2024

Abstract

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual’s sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars—three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.
Keywords: radar; sleep posture; sleep apnea; sleep medicine; polysomnography; ubiquitous health radar; sleep posture; sleep apnea; sleep medicine; polysomnography; ubiquitous health

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MDPI and ACS Style

Lai, D.K.-H.; Tam, A.Y.-C.; So, B.P.-H.; Chan, A.C.-H.; Zha, L.-W.; Wong, D.W.-C.; Cheung, J.C.-W. Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM). Sensors 2024, 24, 5016. https://doi.org/10.3390/s24155016

AMA Style

Lai DK-H, Tam AY-C, So BP-H, Chan AC-H, Zha L-W, Wong DW-C, Cheung JC-W. Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM). Sensors. 2024; 24(15):5016. https://doi.org/10.3390/s24155016

Chicago/Turabian Style

Lai, Derek Ka-Hei, Andy Yiu-Chau Tam, Bryan Pak-Hei So, Andy Chi-Ho Chan, Li-Wen Zha, Duo Wai-Chi Wong, and James Chung-Wai Cheung. 2024. "Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM)" Sensors 24, no. 15: 5016. https://doi.org/10.3390/s24155016

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