Pervasive Systems in Health Care

Kameas, Achilles; Calemis, Ioannis

doi:10.1007/978-0-387-93808-0_12

Cited by 18 publications

(5 citation statements)

References 4 publications

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“…With recent advances in sensor networks research we are already embarking on the path of revolutionary low cost health care monitoring systems embedded within the home and living environments [9] [10]. In particular, AmI systems have the potential to enhance the health care domain dramatically.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Ambient Intelligence in Healthcare

et al. 2013

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Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people’s capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users’ goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

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Section: Introductionmentioning

confidence: 99%

A Survey on Ambient Intelligence in Healthcare

et al. 2013

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“…While narrow applications will hit the mainstream in the coming years, researchers forecast it will take a decade or more to realize ambient computing's vision [59] entirely. This transition requires improvements across a broad technology stack encompassing hardware, infrastructure, systems, algorithms, and interfaces [60].…”

Section: Discussionmentioning

confidence: 99%

“…Virtual assistants also provide company. • Increase accessibility for those requiring accommodations through adaptive interfaces and automation attuned to abilities, disabilities, and changing needs [45].…”

Section: Benefitsmentioning

confidence: 99%

Ambient Computing: The Integration of Technology into Our Daily Lives

Reddy S

2022

J Arti Inte & Cloud Comp

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Ambient computing is an innovative technology that has gained significant traction recently. It involves integrating information technology into our daily routine subtly and unobtrusively. The technology that surrounds us interacts with us naturally and without requiring our conscious attention or explicit commands. This means that we can go about our daily lives while the technology works seamlessly in the background to provide us with the best experiences. This literature review provides an in-depth analysis of the rise of ambient computing, its key characteristics, enabling technologies, applications, use cases, challenges, and outlook. With the integration of artificial intelligence, ubiquitous computing, sensors, connectivity, and human-computer interfaces, ambient computing has the potential to fade into the background and provide seamless and contextualized experiences tailored to individuals' needs and situations. Ambient computing holds great promise in providing an enhanced understanding of the world and allowing us to be more productive and efficient in our daily lives. It has already found many applications, from home automation to healthcare and transportation. However, with all the benefits that ambient computing has to offer, there are still challenges that need to be addressed. These challenges include privacy, security, reliability, accessibility, and human-AI co-existence. Addressing these challenges is crucial to ensure ambient computing can be deployed responsibly and ethically. This review synthesizes current knowledge on ambient computing and highlights areas that require further investigation to promote informed discourse and responsible advancement of this emerging paradigm. Overall, the potential benefits of ambient computing are enormous, and it is essential to continue the research and development of this technology while addressing its challenges and risks.

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“…Kameas and Calemis [24] studied a variety of ubiquitous platforms that assist health-related tasks. ECG, heart rate, and oxygen saturation level may be monitored remotely.…”

Section: Related Workmentioning

confidence: 99%

Ambient Assistive Living for Monitoring the Physical Activity of Diabetic Adults through Body Area Networks

Srinivasu¹,

JayaLakshmi

Jhaveri

et al. 2022

Mobile Information Systems

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The adequate aging hypothesis seeks to help people live longer, healthy lives. Diabetic patients who stay remotely need an infrastructure to monitor them continuously and provide timely treatment. Ambient assisted living (AAL) encourages the establishment of solutions that may help optimize older people’s assistive environment while also reducing their impairments. The blood glucose levels of diabetic patients are continuously monitored by gold oxide sensors placed over the human body. The signals associated with the glucose levels in the human body are plotted over a spectrogram image using the short-time Fourier transform, which is further classified using the deep learning model based on finetuned AlexNet, which has employed random oversampling and batch normalization for better precision in the results. The model classifies the spectrogram images as low and high glucose levels and normal glucose levels. Thereby alarming the caretakers for effective treatment of the individuals. Body area networks (BANs) gather information from biosensors and send it to a domain controller to assist caretakers and physicians in recommending the physical exercises for their clients. Evaluation criteria such as sensitivity and specificity, precision, and Mathew’s correlation coefficient are used to assess the effectiveness of the proposed model in this current diabetes study. The cross-validation of the model at multiple folds is being evaluated to analyze the performance. It is evident from the obtained results that the proposed model has exhibited an acceptable performance in precisely sensing the individuals with abnormal glucose levels.

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Pervasive Systems in Health Care

Cited by 18 publications

References 4 publications

A Survey on Ambient Intelligence in Healthcare

A Survey on Ambient Intelligence in Healthcare

Ambient Computing: The Integration of Technology into Our Daily Lives

Ambient Assistive Living for Monitoring the Physical Activity of Diabetic Adults through Body Area Networks

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