Sleep posture recognition based on machine learning: A systematic review

Li, Xianglin; Gong, Yanfeng; Jin, Xiaoyun; Shang, Peng

doi:10.1016/j.pmcj.2023.101752

Cited by 14 publications

(4 citation statements)

References 60 publications

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“…Sleep stage classification focuses on investigating the fundamental sleep cycles, and any disruption or insufficient time spent in a particular cycle can result in disorders [29]. Other studies focus on monitoring sleep postures, examining body movements and positions such as supine, prone, left, and right, as well as the duration of each posture [30].…”

Section: A Human Physical Signs and Physiological Signsmentioning

confidence: 99%

“…The reasons of lacking datasets for sleep monitoring can be considered in the following aspects. The sleep monitoring devices are difficult to wear and may cause discomfort to the subject, so the collected data may not reflect the subject's natural sleep data [30]. At the same time, the subjects' sleep data contains a lot of personal information, possibly because of which some regulations prevent the data from being disclosed.…”

Section: A Dataset Availabilitymentioning

confidence: 99%

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Review of the Open Data Sets for Contactless Sensing

Liang,

Chen,

et al. 2024

IEEE Internet Things J.

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Recent years have witnessed the increasing popularity and dramatic progress of contactless sensing technologies, which are able to conduct remote signal acquisition without body contact. Both the physical signs and the physiological parameters can be acquired with contactless sensing. This paper introduces popular contactless sensing technologies, explores their application scenarios, and delves into the underlying theoretical principles. It comprehensively reviews the open datasets released in this field, encompassing collection scenarios, sample counts, data formats, and volunteer information. The performance baseline, typical work, and accessible links are also furnished. In addition, it includes discussions on the primary challenges and potential solutions in the context of contactless sensing with open datasets. Finally, suggestions for establishing a high-quality dataset are also given to the community.

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Section: A Human Physical Signs and Physiological Signsmentioning

confidence: 99%

Section: A Dataset Availabilitymentioning

confidence: 99%

Review of the Open Data Sets for Contactless Sensing

Liang,

Chen,

et al. 2024

IEEE Internet Things J.

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“…Various sensors have been developed to monitor sleep postures and behaviors, including body pressure sensors, physiological sensors, cameras (and depth cameras), and wearable devices [10]. The pressure intensity distribution generated by a pressure mat has been utilized to characterize sleep postural behavior and estimated sleep quality [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System

Lai

Leung

et al. 2023

Sensors

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Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants’ data (n = 6) for model validation, and the remaining six participants’ data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique.

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“…Several film companies use sensor-based schemes [5]. There are two primary methods for three-dimensional (3D) pose recognition.…”

Section: Introductionmentioning

confidence: 99%

High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network

Ding

2023

Applied Sciences

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Pose recognition in character animations is an important avenue of research in computer graphics. However, the current use of traditional artificial intelligence algorithms to recognize animation gestures faces hurdles such as low accuracy and speed. Therefore, to overcome the above problems, this paper proposes a real-time 3D pose recognition system, which includes both facial and body poses, based on deep convolutional neural networks and further designs a single-purpose 3D pose estimation system. First, we transformed the human pose extracted from the input image to an abstract pose data structure. Subsequently, we generated the required character animation at runtime based on the transformed dataset. This challenges the conventional concept of monocular 3D pose estimation, which is extremely difficult to achieve. It can also achieve real-time running speed at a resolution of 384 fps. The proposed method was used to identify multiple-character animation using multiple datasets (Microsoft COCO 2014, CMU Panoptic, Human3.6M, and JTA). The results indicated that the improved algorithm improved the recognition accuracy and performance by approximately 3.5% and 8–10 times, respectively, which is significantly superior to other classic algorithms. Furthermore, we tested the proposed system on multiple pose-recognition datasets. The 3D attitude estimation system speed can reach 24 fps with an error of 100 mm, which is considerably less than that of the 2D attitude estimation system with a speed of 60 fps. The pose recognition based on deep learning proposed in this study yielded surprisingly superior performance, proving that the use of deep-learning technology for image recognition has great potential.

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Sleep posture recognition based on machine learning: A systematic review

Cited by 14 publications

References 60 publications

Review of the Open Data Sets for Contactless Sensing

Review of the Open Data Sets for Contactless Sensing

Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System

High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network

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