Sleep position classification from a depth camera using Bed Aligned Maps

Grimm, T; Martínez, Manuel Martínez; Benz, Andreas; Stiefelhagen, Rainer

doi:10.1109/icpr.2016.7899653

Cited by 56 publications

(35 citation statements)

References 24 publications

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“…Also, it was not possible to use it for real sleep scenarios when poses are occluded by a blanket. The current state-ofthe-art in performance for video-based pose classification is described in [26]. The authors used a dimensional reduction technique known as bed aligned maps from depth images.…”

Section: Prior Workmentioning

confidence: 99%

Transfer Learning for Clinical Sleep Pose Detection Using a Single 2D IR Camera

Mohammadi

Enshaeifar

Hilton

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Sleep quality is an important determinant of human health and wellbeing. Novel technologies that can quantify sleep quality at scale are required to enable the diagnosis and epidemiology of poor sleep. One important indicator of sleep quality is body posture. In this paper, we present the design and implementation of a non-contact sleep monitoring system that analyses body posture and movement. Supervised machine learning strategies applied to noncontact vision-based infrared camera data using a transfer learning approach, successfully quantified sleep poses of participants covered by a blanket. This represents the first occasion that such a machine learning approach has been used to successfully detect four predefined poses and the empty bed state during 8-10 hour overnight sleep episodes representing a realistic domestic sleep situation. The methodology was evaluated against manually scored sleep poses and poses estimated using clinical polysomnography measurement technology. In a cohort of 12 healthy participants, we find that a ResNet-152 pre-trained network achieved the best performance compared with the standard de novo CNN network and other pretrained networks. The performance of our approach was better than other video-based methods for sleep pose estimation and produced higher performance compared to the clinical standard for pose estimation using a polysomnography position sensor. It can be concluded that infrared video capture coupled with deep learning AI can be successfully used to quantify sleep poses as well as the transitions between poses in realistic nocturnal conditions, and that this non-contact approach provides superior pose estimation compared to currently accepted clinical methods.

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Section: Prior Workmentioning

confidence: 99%

Transfer Learning for Clinical Sleep Pose Detection Using a Single 2D IR Camera

Mohammadi

Enshaeifar

Hilton

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Numerous studies using 3D sensors, such as Microsoft Kinect One (Microsoft corp., Redmond, Washington, USA), in healthcare applications have been published [20]- [23]. The way 3D technology is presently used in the sleep environment was proposed by T. Grimm et al [24], who suggested a method to estimate four classes of body postures. Lee et al [25] and Yu et al [26] estimated body postures and derived full body movements from posture changes using 3D cameras.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Movements During Sleep Using a 3D Time-of-Flight Camera: Design and Experimental Evaluation

et al. 2020

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Analyses of sleep-related movement disorders have gained importance due to an increase in life expectancy. The present approaches for measuring movements are based on electromyography or accelerometry and provide only local or specific results from muscles/limbs to which sensors have been attached. The motivation of this work was to investigate the detection of a more complete spectrum of sleeprelated movements using a three-dimensional (3D) camera instead of the current conventional methods. In contrast to most of the previously published literature, this method allows for the detection of movements even when patients are covered with a blanket. This is the first work to evaluate movement detection with a clinical dataset and replicate the clinical environment in a laboratory setup. The laboratory setup allowed for the characterization of detectable movements through the determination of speed and amplitude limits. We used the Kinect One time-of-flight sensor to record 3D videos. Movements were quantified based on the temporal depth change in these 3D videos. A computer-controlled lifting table allowed for the controlled simulation of movements. Our algorithm detected movements with amplitude values >3.0 mm and velocity values >3.5 mm/s with an F1 score ≥95%. The shortest reliably detected duration of movement was 350 ms. In an ethically approved clinical study including 44 patients, 93.1% of electromyography-detected leg movements were also found in 3D. A significant correlation (ρ = 0.86) was found between movements detected by the 3D system and polysomnography. The 3D system detected 31.2% more movements than electromyography. In addition to obtaining a broader spectrum of movements not limited to local and muscle/limb-specific movements, the usage of a contactless 3D camera simplifies the recording setup and preserves natural sleeping behavior. The presented 3D system may become useful for diagnostic purposes during sleep studies.

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“…Automatic human detection and its derivative technologies are increasingly applied to various fields, such as the security [1], [2], [3], healthcare [4], [5], and service industries [6]. In particular, human detection in the office environment allows us an in-depth understanding of the status of a team and may provide a hint for improving productivity [7].…”

Section: Introductionmentioning

confidence: 99%

Depth-Based Human Detection Considering Postural Diversity and Depth Missing in Office Environment

Fujimoto

Fujita

2019

IEEE Access

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To realize robust human detection in an actual office work scenario, this paper proposes two ideas using top-view depth cameras. To deal with the changing geometric human shapes caused by body posture (e.g., sitting, standing, and crouching), we propose two features to describe the human upper-back shape, i.e., roundness and size of a height-continuous region. For alleviating the influences of partial loss of depth information caused by occlusions and by the absorption of infrared light, we propose an adaptive feature adjustment algorithm, which utilizes implicitly included information in the missing region. We implemented the proposed algorithm on a system with 13 depth cameras. Application to 100-hours (10 workdays) of actual office data demonstrated that the upper-back features complement the existing head-shoulder features. It also demonstrated that both of the proposals contributed to a more robust human detection and attained 97.7 % accuracy.

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Sleep position classification from a depth camera using Bed Aligned Maps

Cited by 56 publications

References 24 publications

Transfer Learning for Clinical Sleep Pose Detection Using a Single 2D IR Camera

Transfer Learning for Clinical Sleep Pose Detection Using a Single 2D IR Camera

Automated Detection of Movements During Sleep Using a 3D Time-of-Flight Camera: Design and Experimental Evaluation

Depth-Based Human Detection Considering Postural Diversity and Depth Missing in Office Environment

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