Implementation of Smart Monitoring System With Fall Dectector for Elderly Using Iot Technology

Harum, Norharyati; Abidin, Zaheera Zainal; Shah, Wahidah Md; Hassan, Aslinda

doi:10.47839/ijc.17.4.1146

Cited by 15 publications

(5 citation statements)

References 11 publications

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“…1, Malaysia's Ministry of Health Malaysia (2019) depicts that Malaysia occupies the first spot in terms of mortality rates among the population aged between 20 and 79 globally. Several scholarly investigations have proposed that adopting Internet of Things (IoT) healthcare services could be the solution for improved monitoring and healthcare delivery to patients with chronic illnesses [6,34,35]. In this manner, the Internet of Things (IoT) could be used to improve patients' quality of life by reducing the stress placed on their loved ones and hospital visits.…”

Section: A Iot In Healthcarementioning

confidence: 99%

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Behavior Intention of Chronic Illness Patients in Malaysia to Use IoT-based Healthcare Services

Jawad¹,

Hassan²,

Zaidan³

2023

IJACSA

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The Internet of Things (IoT) has emerged as a trend in the healthcare industry to develop innovative solutions that enhance patient outcomes and operational efficiency. Healthcare has become more accessible, affordable, and efficient to sensors, wearables, and health monitors. The healthcare industry's adoption of the Internet of Things is lagging behind other sectors despite its many benefits. This study aims to investigate the extent to which chronic patients in Malaysia are using healthcare services made possible by the Internet of Things. To that end, this study proposes a unified framework to examine how these highlighted factors affect Behavioral Intention (BI) with regard to adopting IoT healthcare services. The innovation here is in bringing together three distinct theories: i) the Technology-Organization-Environment Framework (TOE), which is a framework for understanding how companies adopt new technologies; ii) the Unified Theory of Acceptance and Use of Technology (UTAUT); and iii) the Social Exchange Theory (SE). Patients in Malaysia who are coping with long-term health issues were surveyed online. This study also employs SPSS and Smart Partial Least Square (Smart PLS) for data analysis. Eleven hypothesized predictive components have been investigated. The results showed that chronic illness patients' BI towards adopting IoT solutions was considerably impacted by both individual and technological factors and related aspects. The impact of BI on Use Behaviour (UB) also showed similar outcomes. Moreover, trust somewhat mediates the impact of both individual and technological factors on BI.The findings of this investigation will be beneficial to policymakers and suppliers of healthcare in that country. Additionally, the patients and their family members would gain benefits from the study due to the fact that the delivery of comprehensive treatment, especially in the field of chronic disease management, will be improved through IoT-healthcare services. The Internet of Things will also let medical staff function remotely and professionally.

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Section: A Iot In Healthcarementioning

confidence: 99%

“…The absence of information on users' opinions regarding the utilization and implementation of IoT in the healthcare system is notable. [6], Malaysia is no exception, especially for patients with chronic diseases.…”

Section: Introductionmentioning

confidence: 99%

Behavior Intention of Chronic Illness Patients in Malaysia to Use IoT-based Healthcare Services

Jawad¹,

Hassan²,

Zaidan³

2023

IJACSA

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“…The standard deviation is an index of how near the individual data points cluster around the mean. If we called each data point , an index of dispersion, it would be represented in (8):…”

Section: B Feature Extractionmentioning

confidence: 99%

“…Ambient-based approaches use ambiance sensors, including acoustic sensors, vibration sensors, pressure sensors, and infrared sensors for detecting a fall event [3,4,5]. They also use single or multiple cameras in an indoor environment to track a person's movements and body shape while falling [6,7,8]. One main drawback of ambient sensors is that they limit falls to only those detected in a pre-set area, and this does not seem suitable for people's mobility.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries

Kchouri¹,

Harum²,

Obeid³

et al. 2022

IJACSA

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Falling is a major health issue that can lead to both physical and mental injuries. Detecting falls accurately can reduce the severe effects and improve the quality of life for disabled people. Therefore, it is critical to develop a smart fall detection system. Many approaches have been proposed in wearable-based systems. In these approaches, machine learning techniques have been conducted to provide automatic classification and to improve accuracy. One of the most commonly used algorithms is Support Vector Machine (SVM). However, classical SVM can neither use prior knowledge to process accurate classifications nor solve problems characterized by ambiguity. More specifically, some values of falls are inaccurate and similar to the features of normal activities, which can also greatly impact the performance of the learning ability of SVMs. Hence, it became necessary to look for an effective fall detection method based on a combination of Fuzzy Logic (FL) and SVM algorithms so as to reduce false positive alarms and improve accuracy. In this paper, various training data are assigned to the corresponding membership degrees. Some data points with a high chance of falling are assigned a high degree of membership, yielding a high contribution for SVM decisionmaking. This does not only achieve accurate fall detection, but also reduces the hesitation in labeling the outcomes and improves the heuristic transparency of the SVM. The experimental results achieved 100% specificity and precision, with an overall accuracy of 99.96%. Consequently, the experiment proved to be effective and yielded better results than the conventional approaches.

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“…The fast development in information and communication technology has witnessed the emergence of the Internet of Things (IoT), a technology in which electronic devices and electrical appliances are interconnected and are able to transfer data (1,2). IoT is expected to facilitate and automate information exchange to a broader context of data communication (3,4) which consequently improves the quality of life (5)(6)(7) and business processes (8). IoT applications cover smart homes (9), smart cities (10), smart vehicles (11), healthcare (12), smart grids (13), and smart farming (14), to mention a few.…”

Section: Introductionmentioning

confidence: 99%

A Scoping Study on Lightweight Cryptography Reviews in IoT

Chiadighikaobi¹,

Katuk

2021

Baghdad Sci.J

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The efforts in designing and developing lightweight cryptography (LWC) started a decade ago. Many scholarly studies in literature report the enhancement of conventional cryptographic algorithms and the development of new algorithms. This significant number of studies resulted in the rise of many review studies on LWC in IoT. Due to the vast number of review studies on LWC in IoT, it is not known what the studies cover and how extensive the review studies are. Therefore, this article aimed to bridge the gap in the review studies by conducting a systematic scoping study. It analyzed the existing review articles on LWC in IoT to discover the extensiveness of the reviews and the topics covered. The results of the study suggested that many review studies are classified as overview-types of review focusing on generic LWC. Further, the topics of the reviews mainly focused on symmetric block cryptography, while limited reviews were found on asymmetric-key and hash in LWC. The outcomes of this study revealed that the reviews in LWC in IoT are still in their premature stage and researchers are encouraged to explore by conducting review studies in the less-attended areas. An extensive review of studies that cover these two topics is deemed necessary to establish a balance of scholarly works in LWC for IoT and encourage more empirical research in the area.

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Implementation of Smart Monitoring System With Fall Dectector for Elderly Using Iot Technology

Cited by 15 publications

References 11 publications

Behavior Intention of Chronic Illness Patients in Malaysia to Use IoT-based Healthcare Services

Behavior Intention of Chronic Illness Patients in Malaysia to Use IoT-based Healthcare Services

Fuzzy Support Vector Machine based Fall Detection Method for Traumatic Brain Injuries

A Scoping Study on Lightweight Cryptography Reviews in IoT

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