A pressure map dataset for posture and subject analytics

Pouyan, Maziyar Baran; Birjandtalab, Javad; Heydarzadeh, Mehrdad; Nourani, Mehrdad; Ostadabbas, Sarah

doi:10.1109/bhi.2017.7897206

Cited by 63 publications

(58 citation statements)

References 13 publications

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“…Three sleep datasets were used in the experiment, including sleep bioradiolocation dataset (bioradar) [32], pressure map dataset [33] and polysomnography (PSG) dataset [34].…”

Section: A Datasetsmentioning

confidence: 99%

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

2020

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Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and overfitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: ''https://github.com/zhaoaite/SSRM ''.

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“…Three sleep datasets were used in the experiment, including sleep bioradiolocation dataset (bioradar) [32], pressure map dataset [33] and polysomnography (PSG) dataset [34].…”

Section: A Datasetsmentioning

confidence: 99%

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

2020

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“…The result shows that in case of severe OSApatients, the lateral positioning improves upper airway stabilityduring sleep. In [9] a system is proposed for analyzing posturesand subjects using pressure sensing mats.…”

Section: Sleepingposturesmentioning

confidence: 99%

“…As far from our research, PmatData is the first publicly-available dataset of pressure sensor data whichincludes various sleeping postures. The data was collectedunder IRB approval at the University of Texas at Dallas [18] from thirteen participants in different postures. Informedconsents were signed by all individuals before data collectionand all agreed on anonymous publication of their data for future research.…”

Section: A Data Collection Detailsmentioning

confidence: 99%

Robust Technique to Detect COVID-19 using Chest X-ray Images

Channa

Popescu

Malik

2020

2020 International Conference on E-Health and Bioengineering (EHB)

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“…For some approaches, e.g., image-based approaches, the number of detected positions must be reduced based on the limitations of the method. This normally involves excluding the prone position or combining the prone position with the supine position [16], [17], [21], [22], [30], [113]- [116]. Others consider more complex positions, such as right fetus [17], [67], right yearner [17], right log [67], left fetus [17], [67], left log [67], left yearner [17], supine [17], [67] and prone [67].…”

Section: Sleep Position Recognitionmentioning

confidence: 99%

Computational Sleep Behavior Analysis: A Survey

Fallmann

Chen

2019

IEEE Access

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Sleep is a key marker of health, as it can either be a cause or a consequence. It is traditionally studied in clinical environments using dedicated medical devices. Recent technological developments, e.g., in sensing and data analysis, have led to new approaches for sleep monitoring and assessment, which are attracting increasing attention in the emerging domain of personalized smart healthcare. Nevertheless, a highlevel overview of technology-enabled research on sleep that can inform related communities of the latest developments is lacking. In this paper, we present a comprehensive review to examine the current status of various aspects of technology-based sleep research. We first characterize sleep behavior and key areas of sleep assessment, and we introduce a general review of the methodologies used in this domain. We review the major technological methods and trends associated with sleep monitoring, data collection and sleep behavior analysis, from which strengths and weaknesses are highlighted. Finally, we also discuss challenges and promising directions for future research.

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A pressure map dataset for posture and subject analytics

Cited by 63 publications

References 13 publications

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

Robust Technique to Detect COVID-19 using Chest X-ray Images

Computational Sleep Behavior Analysis: A Survey

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