A motion detection algorithm for video-polysomnography to diagnose sleep disorder

Islam, Zahidul; Nahiyan, K M Talha; Kiber, Md. Adnan

doi:10.1109/iccitechn.2015.7488081

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…With the increase of research in wearable technologies, a new technology called the Internet of Medical Things (IoMT) has emerged [21]. Some applications within this scope are patient monitoring [22], fall detection [23], detection for motion disorder [24], sleep monitoring [21], evaluation of illness degree of a clinical risk level [25], health monitoring [26,27], medical image segmentation [28], attack detection [29], and implantable sensors [30]. Although this application required more expensive hardware but allowed for a more sophisticated service.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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Currently, rapidly developing digital technological innovations affect and change the integrated information management processes of all sectors. The high efficiency of these innovations has inevitably pushed the health sector into a digital transformation process to optimize the technologies and methodologies used to optimize healthcare management systems. In this transformation, the Internet of Things (IoT) technology plays an important role, which enables many devices to connect and work together. IoT allows systems to work together using sensors, connection methods, internet protocols, databases, cloud computing, and analytic as infrastructure. In this respect, it is necessary to establish the necessary technical infrastructure and a suitable environment for the development of smart hospitals. This study points out the optimization factors, challenges, available technologies, and opportunities, as well as the system architecture that come about by employing IoT technology in smart hospital environments. In order to do that, the required technical infrastructure is divided into five layers and the system infrastructure, constraints, and methods needed in each layer are specified, which also includes the smart hospital’s dimensions and extent of intelligent computing and real-time big data analytic. As a result of the study, the deficiencies that may arise in each layer for the smart hospital design model and the factors that should be taken into account to eliminate them are explained. It is expected to provide a road map to managers, system developers, and researchers interested in optimization of the design of the smart hospital system.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of factors affecting IoT-based smart hospital design

2020

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“…Jyothi and Vardhan suggest that motion detection algorithm applies principles of differencing frame where the algorithm compares pixels on how they change location after each frame [9]. A study conducted by Islam, Nahiyan and Kiber also agreed on the concept that considering frames of video as images, motion can be detected by performing absolute image difference [10], [11]. There is a more reliable method based on background subtraction and frame difference, a fusion to more precisely detect motion.…”

Section: Motion Detection Algorithmmentioning

confidence: 99%

Development of a portable community video surveillance system

Fen

2019

IJECE

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In 2016, a crime rate has been evidently increasing particularly in Kuala Lumpur areas, including reports on house break-ins, car thefts, motorcycle thefts and robbery. One way of deterring such cases is by installing CCTV monitoring system in premises such as houses or shops, but this usually requires expensive equipment and installation fees. In this paper a cheaper alternative of a portable community video surveillance system running on Raspberry Pi 3 utilizing OpenCV is presented. The system will detect motion based on image subtraction algorithm and immediately inform users when intruders are detected by sending a live video feed to a Telegram group chat, as well as sound the buzzer alarm on the Raspberry Pi. Additionally, any Telegram group members can request images and recorded videos from the system at any time by sending a get request in Telegram which will be handled by Telegram Bot. This system uses the Pi NoIR camera module as the image acquisition device equipped with a 36 LED infrared illuminator for night vision capability. In addition to the Python language, OpenCV, a computer vision simulation from Intel is also used for image processing tasks. The performance analysis of the completed system is also presented computational complexity while offering improved flexibility. The performance time is also presented, where the whole process is run with a noticeable 3 seconds delay in getting the final output.

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“…Applications include fall detection [8], activity detection for energy saving at homes or offices [9], 24-hour sleep-wake monitoring in narcolepsy [10], a detection system for motion disorders in Autism patients [11], and other uses leveraging IoTs [12][13][14][15][16][17][18]. The methods introduced in [13,14] leverage body sensor nodes powered by human energy harvesting and wireless sensor networks for remote patient monitoring.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things for Sensing: A Case Study in the Healthcare System

et al. 2018

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Medical healthcare is one of the fascinating applications using Internet of Things (IoTs). The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI) signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.

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A motion detection algorithm for video-polysomnography to diagnose sleep disorder

Cited by 9 publications

References 5 publications

Analysis of factors affecting IoT-based smart hospital design

Analysis of factors affecting IoT-based smart hospital design

Development of a portable community video surveillance system

Internet of Things for Sensing: A Case Study in the Healthcare System

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