Wireless health monitoring system for sleepwalking patients

Singhal, Smriiti; Jain, Prem C.

doi:10.1109/natsys.2015.7489083

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…In case of people sharing the room, the system cannot detect the patient's activities precisely. This is similar to the work of Singhal [9], who presented a prototype for monitoring and protecting sleepwalking patients using motion sensors and accelerometers. The motion sensors are installed in the room and at openings such as doors or windows.…”

Section: Monitoring Systemsupporting

confidence: 78%

A System for Sleepwalking Accident Prevention Utilizing the Remote Sensor of Wearable Device

Damkliang

Andritsch²,

Khamkom³

et al. 2020

ECTI-CIT

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Sleepwalking is a type of sleep disorder which originates during deep sleep and results in walking state and performing series of complex behaviors or actions while sleeping. In some cases, sleepwalking patients can injure themselves from their actions such as driving a car or climbing out of a window. In addition, to wake up the sleepwalkers can be difficult. The suddenly waking up and can cause them to be confused or even attack the person who wakes them. Therefore, detecting the sleepwalking incident in an early state can help the caretaker or family members to stop the patients before they harm themselves from any strange, inappropriate, or violent behaviors. In this research, we present a prototype system of sleepwalking detection algorithm and notification system using smart device which work coordinating with wearable device. There are two main groups of users; patients and caretakers. User Activity Sensor (UAS) in the wearable device is utilized for detecting User Activity Data (UAD) which is unusual activities of inducing a sleepwalking patient provided by the Remote Sensor SDK. The system returns the patient UAD states consisting of standing, walking, and running. The smart device accepts the UAD states from the wearable device, performs sleepwalking detection algorithms then, alarms caretakers when the sleepwalking state has already invoked. The system is implemented, built, tested and deployed. The threefold experimental measurement of physical user activites have been performed to validate our proposed sleepwalking detection algorithms. The system correctly detects the sleepwalking states and notifies the caretaker.

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Section: Monitoring Systemsupporting

confidence: 78%

A System for Sleepwalking Accident Prevention Utilizing the Remote Sensor of Wearable Device

Damkliang

Andritsch²,

Khamkom³

et al. 2020

ECTI-CIT

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“…The IoMT system is developed to classify the various vocal fold disorders by acoustic speech signal processing [14]. Sleepwalking patients are monitored and protected by a model that can detect a person falling or trying to go out of doors or windows unconsciously during sleepwalking [15].…”

Section: Literature Surveymentioning

confidence: 99%

Accident Attention System for Somnambulism Patients

Ziyad,

Altulyan,

Liakathunisa

et al. 2023

SCPE

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Promising technologies such as sensors, networking, and edge have led to many smart healthcare solutions to monitor and track patient health status. The health sector is now experiencing a significant transformation from conventional patient care to a smart healthcare environment. Smart health care allows medical professionals to monitor patients remotely and visualize the disease prognosis effectively. The Internet of medical things connect patients, doctors, and medical equipment via wireless networking technologies to process the data with Artificial Intelligence models. One of the domains of automated health care systems is to alert the caregivers and hospital on emergency conditions. This research study is a novel work that aims to help the caregivers of somnambulism patients attend to them in case of emergency. Sleep quality improves the health and work efficiency of any person. The caregivers of sleepwalking patients suffer from lack of sleep as the patient gets active during the night hours. The model is based on fall detection and sleep detection from wearable sensor data. The fall detection model includes feature selection by LASSO and classification by ensemble classifier. The proposed methodology shows improved performance for the fall detection model for all ensemble machine learning classifiers.

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Physical Activity

Moghaddam

Lowe

2018

Health and Wellness Measurement Approaches for Mobile Healthcare

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Wireless health monitoring system for sleepwalking patients

Cited by 5 publications

References 13 publications

A System for Sleepwalking Accident Prevention Utilizing the Remote Sensor of Wearable Device

A System for Sleepwalking Accident Prevention Utilizing the Remote Sensor of Wearable Device

Accident Attention System for Somnambulism Patients

Physical Activity

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