A Study on devices for assisting Alzheimer patients

Surendran, D.; Janet, Jon Paul; Anisha, E.

doi:10.1109/i-smac.2018.8653658

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Beyond general healthcare and into specific ailments, AD and dementia are the most prominent of them. Although AD is a subtype, or a severe, progressed stage of dementia, some studies focus especially on that, such as Surendran et al [15]. Others refer to the general spectra of dementia and AD alike, such as [8,10].…”

Section: Health Focusmentioning

confidence: 99%

“…In addition to both commercial devices and research prototypes, this review also examines pertaining potential security threats and confidentiality issues. Surendran et al [15] explores smart wearable locator band, smart socks, the CleverCare Smart watch, iTraq, MedicAlert Safely Home, PocketFinder, Trax, and wearable cameras.…”

Section: Iot Technologymentioning

confidence: 99%

“…Li et al [4] review smart home and health care solutions, while emphasizing healthcare, rehabilitation, and AAL infrastructure with mobility assistance applications of robotic service platforms, multi-agent systems, and other human machine interfaces. Lee et al [21] explores the field of sustainable wearables, while Surendran et al [15] explores the networking and accuracy of several wearables and cameras. The study in [26] considers the types of sensors in five categories and especially their efficacy in various short studies (non-longitudinal).…”

Section: Review Criteriamentioning

confidence: 99%

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IoT Wearable Sensors and Devices in Elderly Care: A Literature Review

Stavropoulos

Papastergiou

Mpaltadoros

et al. 2020

Sensors

234

115

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The increasing ageing global population is causing an upsurge in ailments related to old age, primarily dementia and Alzheimer’s disease, frailty, Parkinson’s, and cardiovascular disease, but also a general need for general eldercare as well as active and healthy ageing. In turn, there is a need for constant monitoring and assistance, intervention, and support, causing a considerable financial and human burden on individuals and their caregivers. Interconnected sensing technology, such as IoT wearables and devices, present a promising solution for objective, reliable, and remote monitoring, assessment, and support through ambient assisted living. This paper presents a review of such solutions including both earlier review studies and individual case studies, rapidly evolving in the last decade. In doing so, it examines and categorizes them according to common aspects of interest such as health focus, from specific ailments to general eldercare; IoT technologies, from wearables to smart home sensors; aims, from assessment to fall detection and indoor positioning to intervention; and experimental evaluation participants duration and outcome measures, from acceptability to accuracy. Statistics drawn from this categorization aim to outline the current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology.

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Section: Health Focusmentioning

confidence: 99%

Section: Iot Technologymentioning

confidence: 99%

Section: Review Criteriamentioning

confidence: 99%

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IoT Wearable Sensors and Devices in Elderly Care: A Literature Review

Stavropoulos

Papastergiou

Mpaltadoros

et al. 2020

Sensors

234

115

View full text Add to dashboard Cite

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“…State-of-the-art techniques in these primary wearable assistive devices incorporate Global Systems for GSM mobile communication along with G.P.S. tools and techniques that automatically identify the location of the wearer and transfer that location to their guardian device [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Review of Navigation Assistive Tools and Technologies for the Visually Impaired

Messaoudi

Ménélas

Mcheick

2022

Sensors

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The visually impaired suffer greatly while moving from one place to another. They face challenges in going outdoors and in protecting themselves from moving and stationary objects, and they also lack confidence due to restricted mobility. Due to the recent rapid rise in the number of visually impaired persons, the development of assistive devices has emerged as a significant research field. This review study introduces several techniques to help the visually impaired with their mobility and presents the state-of-the-art of recent assistive technologies that facilitate their everyday life. It also analyses comprehensive multiple mobility assistive technologies for indoor and outdoor environments and describes the different location and feedback methods for the visually impaired using assistive tools based on recent technologies. The navigation tools used for the visually impaired are discussed in detail in subsequent sections. Finally, a detailed analysis of various methods is also carried out, with future recommendations.

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“…GPS tracker: Global Positioning System (GPS) tracker is very important for AD patients with dementia [20]. If an AD patient gets missing, this GPS tracker will help to find them out [21].…”

mentioning

confidence: 99%

Explainable AI-based Alzheimer’s Prediction and Management Using Multimodal Data

Jahan¹,

Taher²,

Kaiser³

et al. 2022

Preprint

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According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is raising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. To solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, Freesurfer MRI segmentation data, and psychological data. For Alzheimer’s disease vs cognitively normal prediction, the random forest classifier provides 100% accuracy. Furthermore, Alzheimer’s disease and non-Alzheimer’s dementia should be classified properly because their symptoms are similar. To the best of our knowledge, we are the first to present a three-class classification on Alzheimer’s disease vs cognitively normal vs non-Alzheimer’s dementia and achieved 99.86% accuracy using an ensemble model. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work..

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A Study on devices for assisting Alzheimer patients

Cited by 8 publications

References 7 publications

IoT Wearable Sensors and Devices in Elderly Care: A Literature Review

IoT Wearable Sensors and Devices in Elderly Care: A Literature Review

Review of Navigation Assistive Tools and Technologies for the Visually Impaired

Explainable AI-based Alzheimer’s Prediction and Management Using Multimodal Data

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