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Hong W. Twistable and Stretchable Nasal Patch for Monitoring Sleep-Related Breathing Disorders Based on a Stacking Ensemble Learning Model. ACS APPLIED MATERIALS & INTERFACES 2024; 16:47337-47347. [PMID: 39192683 DOI: 10.1021/acsami.4c11493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces learning ability and memory. The existing polysomnography used to measure sleep disorders is executed in an unfamiliar environment, which may result in sleep patterns that are different from usual, reducing accuracy. This study reports a machine learning-based personalized twistable patch system that can simply measure obstructive sleep apnea syndrome in daily life. The stretchable patch attaches directly to the nose in an integrated form factor, detecting sleep-disordered breathing by simultaneously sensing microscopic vibrations and airflow in the nasal cavity and paranasal sinuses. The highly sensitive multichannel patch, which can detect airflow at the level of 0.1 m/s, has flexibility via a unique slit pattern and fabric layer. It has linearity with an R2 of 0.992 in the case of the amount of change according to its curvature. The stacking ensemble learning model predicted the degree of sleep-disordered breathing with an accuracy of 92.9%. The approach demonstrates high sleep disorder detection capacity and proactive visual notification. It is expected to help as a diagnostic platform for the early detection of chronic diseases such as cerebrovascular disease and diabetes.
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Affiliation(s)
- Wonki Hong
- Department of Digital Healthcare, Daejeon University, Daejeon 34520, Republic of Korea
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2
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Zhu Y, Hong H, Wang W. Privacy-Protected Contactless Sleep Parameters Measurement Using a Defocused Camera. IEEE J Biomed Health Inform 2024; 28:4660-4673. [PMID: 38696292 DOI: 10.1109/jbhi.2024.3396397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
Sleep monitoring plays a vital role in various scenarios such as hospitals and living-assisted homes, contributing to the prevention of sleep accidents as well as the assessment of sleep health. Contactless camera-based sleep monitoring is promising due to its user-friendly nature and rich visual semantics. However, the privacy concern of video cameras limits their applications in sleep monitoring. In this paper, we explored the opportunity of using a defocused camera that does not allow identification of the monitored subject when measuring sleep-related parameters, as face detection and recognition are impossible on optically blurred images. We proposed a novel privacy-protected sleep parameters measurement framework, including a physiological measurement branch and a semantic analysis branch based on ResNet-18. Four important sleep parameters are measured: heart rate (HR), respiration rate (RR), sleep posture, and movement. The results of HR, RR, and movement have strong correlations with the reference (HR: R = 0.9076; RR: R = 0.9734; Movement: R = 0.9946). The overall mean absolute errors (MAE) for HR and RR are 5.2 bpm and 1.5 bpm respectively. The measurement of HR and RR achieve reliable estimation coverage of 72.1% and 93.6%, respectively. The sleep posture detection achieves an overall accuracy of 94.5%. Experimental results show that the defocused camera is promising for sleep monitoring as it fundamentally eliminates the privacy issue while still allowing the measurement of multiple parameters that are essential for sleep health informatics.
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He C, Fang Z, Liu S, Wu H, Li X, Wen Y, Lin J. A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals. Heliyon 2024; 10:e31839. [PMID: 38868074 PMCID: PMC11168323 DOI: 10.1016/j.heliyon.2024.e31839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/31/2024] [Accepted: 05/22/2024] [Indexed: 06/14/2024] Open
Abstract
People spend approximately one-third of their lives in sleep, but more and more people are suffering from sleep disorders. Sleep posture is closely related to sleep quality, so related detection is very significant. In our previous work, a smart flexible sleep monitoring belt with MEMS triaxial accelerometer and pressure sensor has been developed to detect the vital signs, snore events and sleep stages. However, the method for sleep posture detection has not been studied. Therefore, to achieve high performance, low cost and comfortable experience, this paper proposes a smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals measured by a MEMS Inertial Measurement Unit (IMU). Statistical analysis and wavelet packet transform are applied for the feature extraction of the vital sign signals. Then the algorithm of recursive feature elimination with cross-validation is introduced to further extract the key features. Besides, machine learning models with 10-fold cross validation process, such as decision tree, random forest, support vector machine, extreme gradient boosting and adaptive boosting, were adopted to recognize the sleep posture. 15 subjects were recruited to participate the experiment. Experimental results demonstrate that the detection accuracy of the random forest algorithm is the highest among the five machine learning models, which reaches 96.02 %. Therefore, the proposed sleep posture detection method based on the flexible sleep monitoring belt is feasible and effective.
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Affiliation(s)
- Chunhua He
- School of Computer, Guangdong University of Technology, Guangzhou, 510000, PR China
| | - Zewen Fang
- School of Computer, Guangdong University of Technology, Guangzhou, 510000, PR China
| | - Shuibin Liu
- School of Computer, Guangdong University of Technology, Guangzhou, 510000, PR China
| | - Heng Wu
- School of Automation, Guangdong University of Technology, Guangzhou, 510000, PR China
| | - Xiaoping Li
- School of Computer, Guangdong University of Technology, Guangzhou, 510000, PR China
| | - Yangxing Wen
- The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, PR China
| | - Juze Lin
- Guangdong Provincial People's Hospital, Guangzhou, 510080, Guangdong, PR China
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4
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Yang B, Yang S, Zhu X, Qi M, Li H, Lv Z, Cheng X, Wang F. Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:6186. [PMID: 37448035 DOI: 10.3390/s23136186] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/01/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023]
Abstract
Artificial intelligence technologies such as computer vision (CV), machine learning, Internet of Things (IoT), and robotics have advanced rapidly in recent years. The new technologies provide non-contact measurements in three areas: indoor environmental monitoring, outdoor environ-mental monitoring, and equipment monitoring. This paper summarizes the specific applications of non-contact measurement based on infrared images and visible images in the areas of personnel skin temperature, position posture, the urban physical environment, building construction safety, and equipment operation status. At the same time, the challenges and opportunities associated with the application of CV technology are anticipated.
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Affiliation(s)
- Bin Yang
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Shuang Yang
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Xin Zhu
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Min Qi
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - He Li
- School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Zhihan Lv
- Department of Game Design, Faculty of Arts, Uppsala University, SE-62167 Uppsala, Sweden
| | - Xiaogang Cheng
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210042, China
| | - Faming Wang
- Department of Biosystems, KU Leuven, 3001 Leuven, Belgium
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Zhu Y, Zeng Y, Huang D, Huang J, Lu H, Wang W. Occlusion-robust Sleep Posture Detection using Body Rolling Motion in a Video. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38082939 DOI: 10.1109/embc40787.2023.10340050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
It has been reported that the monitoring of sleep postures is useful for the treatment and prevention of sleep diseases such as obstructive sleep apnea and heart failure. Camera-based sleep posture detection is attractive for the nature of comfort and convenience of use. However, the main challenge is to detect postures from images of the body that are occluded by bed sheets or covers. To address this issue, we propose a novel occlusion-robust sleep posture detection method exploiting the body rolling motion in a video. It uses the head orientation to indicate the posture direction (supine, left or right lateral), triggered by the full-body rolling motion (as a sign of posture change). The experimental results show that our proposed method, as compared with the state-of-the-art approaches such as skeleton-based (MediaPipe) and full-image ResNet based methods, obtained clear improvements on sleep posture detection with heavy body occlusions, with an averaged precision, recall and F1-score of 0.974, 0.993 and 0.983, respectively. The next step is to integrate the sleep posture detection algorithm into a camera-based sleep monitoring system for clinical validations.
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Yin J, Xu J, Ren TL. Recent Progress in Long-Term Sleep Monitoring Technology. BIOSENSORS 2023; 13:395. [PMID: 36979607 PMCID: PMC10046225 DOI: 10.3390/bios13030395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/11/2023] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children's growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.
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Affiliation(s)
- Jiaju Yin
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jiandong Xu
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Tian-Ling Ren
- School of Integrated Circuits, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing 100084, China
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Cheng X, Hu F, Yang B, Wang F, Olofsson T. Contactless sleep posture measurements for demand-controlled sleep thermal comfort: A pilot study. INDOOR AIR 2022; 32:e13175. [PMID: 36567523 DOI: 10.1111/ina.13175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/18/2022] [Accepted: 10/24/2022] [Indexed: 06/17/2023]
Abstract
Thermal comfort during sleep is essential for both sleep quality and human health while sleeping. There are currently few effective contactless methods for detecting the sleep thermal comfort at any time of day or night. In this paper, a vision-based detection approach for human thermal comfort while sleeping was proposed, which is intended to avoid overcooling/overheating supply, meet the thermal comfort needs of human sleep, and improve human sleep quality and health. Based on 438 valid questionnaire surveys, 10 types of thermal comfort sleep postures were summarized. By using a large number of data captured, a fundamental framework of detection algorithm was constructed to detect human sleeping postures, and corresponding weighting model was established. A total of 2.65 million frames of posture data in natural sleep status were collected, and thermal comfort-related sleep postures dataset was created. Finally, the robustness and effectiveness of the proposed algorithm were validated. The validation results show that the sleeping posture and human skeleton keypoints can be used for estimating sleeping thermal comfort, and the the quilt coverage area can be fused to improve the detection accuracy.
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Affiliation(s)
- Xiaogang Cheng
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
- Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden
| | - Fei Hu
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Bin Yang
- Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden
- School of Energy and Safety Engineering, Tianjing Chengjian University, Tianjin, China
| | - Faming Wang
- Department of Biosystems (BIOSYST), KU Leuven, Leuven, Belgium
| | - Thomas Olofsson
- Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden
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Li YY, Wang SJ, Hung YP. A Vision-Based System for In-Sleep Upper-Body and Head Pose Classification. SENSORS 2022; 22:s22052014. [PMID: 35271162 PMCID: PMC8914692 DOI: 10.3390/s22052014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/18/2022] [Accepted: 03/01/2022] [Indexed: 11/16/2022]
Abstract
Sleep quality is known to have a considerable impact on human health. Recent research shows that head and body pose play a vital role in affecting sleep quality. This paper presents a deep multi-task learning network to perform head and upper-body detection and pose classification during sleep. The proposed system has two major advantages: first, it detects and predicts upper-body pose and head pose simultaneously during sleep, and second, it is a contact-free home security camera-based monitoring system that can work on remote subjects, as it uses images captured by a home security camera. In addition, a synopsis of sleep postures is provided for analysis and diagnosis of sleep patterns. Experimental results show that our multi-task model achieves an average of 92.5% accuracy on challenging datasets, yields the best performance compared to the other methods, and obtains 91.7% accuracy on the real-life overnight sleep data. The proposed system can be applied reliably to extensive public sleep data with various covering conditions and is robust to real-life overnight sleep data.
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Affiliation(s)
- Yan-Ying Li
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10167, Taiwan;
- Correspondence:
| | - Shoue-Jen Wang
- Tainan National University of the Arts, Tainan 72045, Taiwan;
| | - Yi-Ping Hung
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10167, Taiwan;
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Manullang MCT, Lin YH, Lai SJ, Chou NK. Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:7777. [PMID: 34883780 PMCID: PMC8659982 DOI: 10.3390/s21237777] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/06/2021] [Accepted: 11/19/2021] [Indexed: 01/03/2023]
Abstract
Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.
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Affiliation(s)
- Martin Clinton Tosima Manullang
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan; (M.C.T.M.); (S.-J.L.)
- Department of Informatics, Institut Teknologi Sumatera, South Lampung Regency 35365, Indonesia
| | - Yuan-Hsiang Lin
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan; (M.C.T.M.); (S.-J.L.)
| | - Sheng-Jie Lai
- Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan; (M.C.T.M.); (S.-J.L.)
| | - Nai-Kuan Chou
- Department of Cardiovascular Surgery, National Taiwan University Hospital, Taipei 10002, Taiwan
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An Overview on Analyzing Deep Learning and Transfer Learning Approaches for Health Monitoring. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021. [DOI: 10.1155/2021/5552743] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
With the rise and advancement of technology, early detection and involvement in health-associated monitoring through home control are growing with population aging. The expansion of healthy life expectations is progressively significant due to the speedy aging of the world population. The patient requires early and home-based treatment to detect and prevent disease on time and with less effort. Home-based health monitoring has been considered the need of a smart home. The services of health monitoring can facilitate the patient by collecting and analyzing the data of health for tackling diverse complex issues of health at a large scale. Health monitoring is a sustainable progression of clinical trials for ensuring that health is monitored according to the defined protocol and standard operating procedures. Various scenarios can be considered for monitoring health and are performed through experts of the field. Healthcare systems are having large-scale infrastructure of electronic devices, medical information systems, wearable and smart devices, medical records, and handheld devices. The growth in medical infrastructure, combined with the development of computational approaches in healthcare, has empowered practitioners and researchers to devise a novel solution in the innovative spectra. A detailed report of the existing literature in terms of deep learning and transfer learning is the dire need and facilitating of modern healthcare. To overcome these limitations, therefore, the proposed study presents a comprehensive review of the existing approaches, techniques, and methods associated with deep learning and transfer learning for health monitoring. This review will help researchers to formulate new ideas for facilitating healthcare based on the existing evidence.
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