Wireless health monitoring system for sleepwalking patients
01 Dec 2015-pp 1-6
TL;DR: The prototype can detect when a person falls or trying to go out of doors, windows etc unconsciously during sleepwalking and send a message to the concerned authorities to prevent any mishap.
Abstract: In today's fast paced life, getting a good and sound sleep has become very difficult resulting in increased cases of sleepwalk. Sleepwalking is much more dangerous than what it appears to be. Many incidents have been reported in which people have performed extremely dangerous activities during sleepwalking viz. suicide, murder, robbery or any other criminal action. This paper aims to develop a prototype for continuous monitoring and protection of sleepwalking patients. The prototype can detect when a person falls or trying to go out of doors, windows etc. unconsciously during sleepwalking. Accelerometer and motion sensor have been used for detecting fall and motion respectively. When an event happens, an alarm sounds and a message is sent on a registered mobile number which is already stored in the microcontroller and simultaneously a call is sent on another mobile number using wireless cellular system, thus alerting the concerned authorities to prevent any mishap. Wireless personal area network WPAN (IEEE802.15.4/ZigBee 2.4 GHz RF Modem) has been used for indoor wireless communication in the prototype.
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14 Mar 2020
TL;DR: This research presents a prototype system of sleepwalking detection algorithm and notification system using smart device which work coordinating with wearable device and correctly detects the sleepwalking states and notifies the caretaker.
Abstract: 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.
1 citations
Additional excerpts
...This is similar to the work of Singhal [9], who presented a prototype for monitoring and protecting sleepwalking patients using motion sensors and accelerometers....
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01 Mar 2017
TL;DR: The proposed system is the next stage in home medical system with the presence of wireless technology, it helps patients in unprecedented ways with helping them get real time medical results which may play an important hand in emergency situations.
Abstract: The proposed system is the next stage in home medical system. With the presence of wireless technology, it helps patients in unprecedented ways with helping them get real time medical results which may play an important hand in emergency situations. Wireless Home Health Monitoring System is a home-based monitoring system which measures the oxygen saturation in the blood, respiratory rate, body temperature, electrical activity of heart, blood glucose levels, blood pressure and health of muscles in the safety of our homes. The data collected from the signals are transmitted wirelessly with the help of the Bluetooth module that is connected to the ATMEL microcontroller and is later processed with the help of LabVIEW.
TL;DR: In this article, two cases of zolpidem toxicity in 2016 in Tehran are presented They had both taken 20 mg of Z-drug and as a result, they began to develop an altered level of mentality.
Abstract: Introduction: The Z-drug zolpidem is an imidazopyridine hypnotic that is prescribed widely for short-term treatment of sleeping problems However, there have been various cases affected by the side effects of this medication Case Presentation: Further to the extant reports of zolpidem adverse reactions, in the current manuscript, two cases of zolpidem toxicity in 2016 in Tehran are presented They had both taken 20 mg of zolpidem and as a result, they began to develop an altered level of mentality Conclusions: Zolpidem use may affect the mental status and results in performing unconscious behaviors
References
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TL;DR: The WINS network represents a new monitoring and control capability for applications in such industries as transportation, manufacturing, health care, environmental oversight, and safety and security, and opportunities depend on development of a scalable, low-cost, sensor-network architecture.
Abstract: W ireless integrated network sensors (WINS) provide distributed network and Internet access to sensors, controls, and processors deeply embedded in equipment, facilities, and the environment. The WINS network represents a new monitoring and control capability for applications in such industries as transportation, manufacturing, health care, environmental oversight, and safety and security. WINS combine microsensor technology and low-power signal processing, computation, and low-cost wireless networking in a compact system. Recent advances in integrated circuit technology have enabled construction of far more capable yet inexpensive sensors, radios, and processors, allowing mass production of sophisticated systems linking the physical world to digital data networks [2–5]. Scales range from local to global for applications in medicine, security, factory automation, environmental monitoring, and condition-based maintenance. Compact geometry and low cost allow WINS to be embedded and distributed at a fraction of the cost of conventional wireline sensor and actuator systems. WINS opportunities depend on development of a scalable, low-cost, sensor-network architecture. Such applications require delivery of sensor information to the user at a low bit rate through low-power transceivers. Continuous sensor signal processing enables the constant monitoring of events in an environment in which short message packets would suffice. Future applications of distributed embedded processors and sensors will require vast numbers of devices. Conventional methods of sensor networking represent an impractical demand on cable installation and network bandwidth. Processing at the source would drastically reduce the financial, computational, and management burden on communication system
3,415 citations
01 Jan 2007
TL;DR: A physiological profile approach to falls risk assessment and prevention and strategies for prevention - from research into practice are put into practice.
747 citations
22 Oct 2007
TL;DR: The difficulty to compare the performances of the different systems due to the lack of a common framework is pointed out and a procedure for this evaluation is proposed.
Abstract: Fall detection of the elderly is a major public health problem. Thus it has generated a wide range of applied research and prompted the development of telemonitoring systems to enable the early diagnosis of fall conditions. This article is a survey of systems, algorithms and sensors, for the automatic early detection of the fall of elderly persons. It points out the difficulty to compare the performances of the different systems due to the lack of a common framework. It then proposes a procedure for this evaluation.
581 citations
"Wireless health monitoring system f..." refers background in this paper
...Many false alarms were observed [10], hence it is not a suitable....
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TL;DR: A generic framework for the automated classification of human movements using an accelerometry monitoring system is introduced and a classifier to identify basic movements from the signals obtained from a single, waist-mounted triaxial accelerometer is developed.
Abstract: A generic framework for the automated classification of human movements using an accelerometry monitoring system is introduced. The framework was structured around a binary decision tree in which movements were divided into classes and subclasses at different hierarchical levels. General distinctions between movements were applied in the top levels, and successively more detailed subclassifications were made in the lower levels of the tree. The structure was modular and flexible: parts of the tree could be reordered, pruned or extended, without the remainder of the tree being affected. This framework was used to develop a classifier to identify basic movements from the signals obtained from a single, waist-mounted triaxial accelerometer. The movements were first divided into activity and rest. The activities were classified as falls, walking, transition between postural orientations, or other movement. The postural orientations during rest were classified as sitting, standing or lying. In controlled laboratory studies in which 26 normal, healthy subjects carried out a set of basic movements, the sensitivity of every classification exceeded 87%, and the specificity exceeded 94%; the overall accuracy of the system, measured as the number of correct classifications across all levels of the hierarchy, was a sensitivity of 97.7% and a specificity of 98.7% over a data set of 1309 movements.
520 citations
"Wireless health monitoring system f..." refers background in this paper
...Rather, waist is considered as a good body part to place accelerometer [7] and the device can be easily attached to some existing waist band [8]....
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TL;DR: A proof of concept to an automatic fall detection system for elderly people based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events.
Abstract: Falls are a major risk for the elderly people living independently. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In the last two decades, several technological solutions for detection of falls were published, but most of them suffer from critical limitations. In this paper, we present a proof of concept to an automatic fall detection system for elderly people. The system is based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events. The classification is based on special features like shock response spectrum and mel frequency ceptral coefficients. For the simulation of human falls, we have used a human mimicking doll: ldquoRescue Randy.rdquo The proposed solution is unique, reliable, and does not require the person to wear anything. It is designed to detect fall events in critical cases in which the person is unconscious or in a stress condition. From the preliminary research, the proposed system can detect human mimicking dolls falls with a sensitivity of 97.5% and specificity of 98.6%.
331 citations
"Wireless health monitoring system f..." refers methods in this paper
...[11] used the vibration and sound generated when a fall happens using an accelerometer and a microphone....
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