Sensor Enhanced Health Information Systems: Issues and Challenges

Adam, Abubakar; Abubakar, Adamu; Mahmud, Murni

doi:10.3991/ijim.v13i01.7037

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…Fake authentication messages on network infrastructure can cause DDoS attacks [77]. WAN gateway includes wireless routers, access points and wireless connection, allowing to connect with other networks with data traffic issues, which can cause security breaches in data transmission handling [105].…”

Section: Vulnerabilities In Network Technologiesmentioning

confidence: 99%

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Kaur,

Shahrestani,

Ruan

2024

Comput. Networks Commun.

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Wearable Wireless Sensor Network (WWSN) devices are widely used in healthcare to monitor health data. However, when WWSN users transmit their data to healthcare professionals or third parties over wireless connections, they face privacy and security vulnerabilities. This paper aims to identify the unsolved privacy and security challenges in wearable sensor devices in healthcare, especially the aspects overlooked by previous research. The main research question is: What are the unsolved privacy and security challenges in wearable sensor devices in healthcare, and what are their implications for users and healthcare professionals? This systematic review employs specific keywords to search for relevant publications on bibliographic databases, including Google Scholar, Scopus, IEEE Xplore, and Web of Science. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) charts helped in screening and summarising the selected papers. The results highlight the critical areas that can make WWSNs vulnerable to security attacks. The findings examine the security and privacy issues of wearable sensor devices in cloud computing, fog computing, the Internet of Things (IoT) and the like. Many studies examine IoT due to its privacy and security challenges, especially regarding handling extensive data, using public channels, deploying advanced technologies, managing sharing policies alongside the growing number of wireless devices, and protecting data from hackers. These challenges seriously threaten the confidentiality, integrity, and availability of health data transmitted by WWSN users to healthcare professionals or third parties in cloud-based environments and IoT and are exacerbated by limited resources. The significant findings thereby focus on unresolved areas in IoT. This paper aims to safeguard against cyber-attacks on healthcare and increase users' adoption rate of WWSN devices.

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Section: Vulnerabilities In Network Technologiesmentioning

confidence: 99%

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Kaur,

Shahrestani,

Ruan

2024

Comput. Networks Commun.

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“…Other than that, Torti et al also detailed the implementation of 'Recurrent Neural Network (RNN)' algorithms for regulated devices on a Microcontroller Unit, with the help of a tri-axial accelerometer for detecting fall [35]. For a comprehensive review, readers are advised to consult [37,38].…”

Section: 2mentioning

confidence: 99%

Wearable Smart Phone Sensor Fall Detection System

Habaebi

Yusoff

Ishak

et al. 2022

Int. J. Interact. Mob. Technol.

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One of the most important measures that developed countries need along with economic success is the provision of contemporary health services for the elderly. Inadequate care for the elderly can put them in danger of falls with serious injury or even death. When an older person falls due to illness, immediate lack of care may lead to death. In this paper, a wearable smart phone sensor fall event detection system is introduced. The proposed system utilizes real-time raw sensory accelerometer, gyroscope and gravity data collected from the AndroSensor App and is then processed and applied to a machine learning classifier. The smart phone is positioned at the waistline to yield the most accurate fall detection results. The system is trained and can detect common activities, like sleeping, walking, sitting, jogging, and events, such as falling in any direction. An full accuracy is almost accomplished using Support Vector Machine classifier in comparison with other classifiers as addressed by other previous research works. Nevertheless, the system requires more validation with elderly people under the supervision of caregivers in a controlled environment.

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“…Graphical System Design is a revolutionary approach to solving design challenges that combine intuitive, graphical programming and flexible, commercial off the shelf hardware while still allowing customization. [1] Graphical System Design blends the skills of an engineering architecture specialist with a technical expert, such as a medical device expert, to drive development.…”

Section: Electronic Sensor Kitmentioning

confidence: 99%

Emergency Notification Using Combination Algorithm with Recognition ECG Signal

Naijit

2020

Int. J. Onl. Eng.

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<p class="p1">Intensive Care Unit (ICU) Rooms usually have several detectors attached to each patient providing intensive care, and several processors control and interpret. If the processor detects an abnormality, the medical professional office will be alerted. Nevertheless, many patients with heart disease are concerned with day-to-day behaviors such as hard work, battle, exercise, shock, fight, and war. Become due to clinical depression and erectile impotence this induces anxiety and fear. The boundaries of your heart muscle and coronary strength are unclear. They want a warning that is quick and accurate before they lose control. We develop signal recognition for an algorithm that is very fast and accurate. It helps alert patients to avoid the operation of risk. Nevertheless, it is able to transfer information from the heartbeat network to the doctor's guidance system. The research would analyze the 200 signals from multiple ECG signals. Integration Component Diagnosis (ICD) is capable of extraordinary reliability of identification than the 17.10-41.93 average percentage of the Automata Matching Process which takes less time than the other 29.37 average percentage process and can alert within 12 seconds. It cannot be identified in a pattern other than without the detection of an ECG signal. In the experimental, the signal is used to distinguish 20 ECG signal patterns.</p>

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Sensor Enhanced Health Information Systems: Issues and Challenges

Cited by 5 publications

References 36 publications

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Wearable Smart Phone Sensor Fall Detection System

Emergency Notification Using Combination Algorithm with Recognition ECG Signal

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