Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection
Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and be...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/181549 |
| _version_ | 1824453302684745728 |
|---|---|
| author | Hu, Dikun Gao, Weidong Ang, Kai Keng Hu, Mengjiao Chuai, Gang Huang, Rong |
| author2 | College of Computing and Data Science |
| author_facet | College of Computing and Data Science Hu, Dikun Gao, Weidong Ang, Kai Keng Hu, Mengjiao Chuai, Gang Huang, Rong |
| author_sort | Hu, Dikun |
| collection | NTU |
| description | Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach. |
| first_indexed | 2025-02-19T03:04:16Z |
| format | Journal Article |
| id | ntu-10356/181549 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:04:16Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1815492024-12-09T05:06:42Z Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection Hu, Dikun Gao, Weidong Ang, Kai Keng Hu, Mengjiao Chuai, Gang Huang, Rong College of Computing and Data Science Institute for Infocomm Research, A*STAR Computer and Information Science Sparse sensor-based Sleep posture detection Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S3CNN) for detecting sleep posture. This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S3CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S3CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach. Published version This research was supported by a project of Guangdong Province under Grant No. 2022B1515130009 and the Special Subject on Agriculture and Social Development under Grant No. 2023B03J0172. This research is supported by the Ministry of Science and Technology of China. 2024-12-09T05:06:41Z 2024-12-09T05:06:41Z 2024 Journal Article Hu, D., Gao, W., Ang, K. K., Hu, M., Chuai, G. & Huang, R. (2024). Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection. Sensors, 24(15), 4833-. https://dx.doi.org/10.3390/s24154833 1424-8220 https://hdl.handle.net/10356/181549 10.3390/s24154833 39123879 2-s2.0-85200915239 15 24 4833 en Sensors © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
| spellingShingle | Computer and Information Science Sparse sensor-based Sleep posture detection Hu, Dikun Gao, Weidong Ang, Kai Keng Hu, Mengjiao Chuai, Gang Huang, Rong Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection |
| title | Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection |
| title_full | Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection |
| title_fullStr | Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection |
| title_full_unstemmed | Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection |
| title_short | Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection |
| title_sort | smart sleep monitoring sparse sensor based spatiotemporal cnn for sleep posture detection |
| topic | Computer and Information Science Sparse sensor-based Sleep posture detection |
| url | https://hdl.handle.net/10356/181549 |
| work_keys_str_mv | AT hudikun smartsleepmonitoringsparsesensorbasedspatiotemporalcnnforsleepposturedetection AT gaoweidong smartsleepmonitoringsparsesensorbasedspatiotemporalcnnforsleepposturedetection AT angkaikeng smartsleepmonitoringsparsesensorbasedspatiotemporalcnnforsleepposturedetection AT humengjiao smartsleepmonitoringsparsesensorbasedspatiotemporalcnnforsleepposturedetection AT chuaigang smartsleepmonitoringsparsesensorbasedspatiotemporalcnnforsleepposturedetection AT huangrong smartsleepmonitoringsparsesensorbasedspatiotemporalcnnforsleepposturedetection |