CARE: Context-awareness for elderly care

Klakegg, Simon; Asare, Kennedy Opoku; Berkel, Niels van; Visuri, Aku; Ferreira, Eija; Hosio, Simo; Gonçalves, Jorge; Huttunen, Hanna Leena; Ferreira, Denzil

doi:10.1007/s12553-020-00512-8

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…Lastly, systems targeted for medical grade applications are often costly, and in addition to being costly, context-aware medical systems may be difficult to scale given the aforementioned data requirements. However, it appears that a sub-class of consumer-oriented contextaware software applications (e.g., personalized mobile apps for depression monitoring [29]) may not suffer from these cost and scalability problems by leveraging hardware from smartphones and user inputs for training data [19,22,24,25,29,34].…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Context-Aware Medical Systems within Healthcare Environments: A Systematic Scoping Review to Identify Subdomains and Significant Medical Contexts

Zon

Ganesh

Deen

et al. 2023

IJERPH

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Context awareness is a field in pervasive computing, which has begun to impact medical systems via an increasing number of healthcare applications that are starting to use context awareness. The present work seeks to determine which contexts are important for medical applications and which domains of context-aware applications exist in healthcare. A systematic scoping review of context-aware medical systems currently used by patients or healthcare providers (inclusion criteria) was conducted between April 2021 and June 2023. A search strategy was designed and applied to Pub Med, EBSCO, IEEE Explore, Wiley, Science Direct, Springer Link, and ACM, articles from the databases were then filtered based on their abstract, and relevant articles were screened using a questionnaire applied to their full texts prior to data extraction. Applications were grouped into context-aware healthcare application domains based on past reviews and screening results. A total of 25 articles passed all screening levels and underwent data extraction. The most common contexts used were user location (8 out of 25 studies), demographic information (6 out of 25 studies), movement status/activity level (7 out of 25 studies), time of day (5 out of 25 studies), phone usage patterns (5 out of 25 studies), lab/vitals (7 out of 25 studies), and patient history data (8 out of 23 studies). Through a systematic review process, the current study determined the key contexts within context-aware healthcare applications that have reached healthcare providers and patients. The present work has illuminated many of the early successful context-aware healthcare applications. Additionally, the primary contexts leveraged by these systems have been identified, allowing future systems to focus on prioritizing the integration of these key contexts.

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Section: Limitations and Future Researchmentioning

confidence: 99%

Context-Aware Medical Systems within Healthcare Environments: A Systematic Scoping Review to Identify Subdomains and Significant Medical Contexts

Zon

Ganesh

Deen

et al. 2023

IJERPH

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“…Further, ontology and rulebased models were also exploited to provide appropriate services to the elderly based on their psychological profile and current activities. A context-aware sensor system (CARE) is presented for nurses in nursing homes through an Android tablet application that has utilized sensors to improve elderly residents' care service [16]. Keeping in view the serious consequences of fall and fall-related injuries, a private real-time and context-aware fall detection system is proposed for elderly people that consists of a smart carpet with a sensor pad placed under a carpet to detect falls and alert health care personnel [21].…”

Section: Ambient Assisted Living For Elderly Activities Monitoringmentioning

confidence: 99%

Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Syed

Alsaeeedi

Jabeen³

et al. 2022

AIS

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Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.

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“…The increasing burden of ageing population is also claimed to drive innovation and application of technology (e.g., Pekkarinen et al, 2020) in the care sector. For instance, data management tools and sensor app infrastructure could assist care managers in measuring residents' and nurses' behaviours and increase their awareness of the potential quality match between residents' needs and care provisions (Klakegg et al, 2020). Smart care can be provided with health, nursing, safety, and auxiliary support for independent, assisted, or dependent older residents.…”

Section: An Integration-based Approachmentioning

confidence: 99%

Present and Future Trends of Sustainable Eldercare Services in China

Marinelli

Zhang²,

Ying

2022

Population Ageing

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Ageing population is one of the most fundamental socio-economic transformations of the twenty-first century, with significant policy implications. China, the world’s most populous nation, is no exception. The necessity for cost-effective, culture-appropriate and sustainable eldercare services is one of the Government’s priorities, in both present and future. This research uses a focus-group interviews methodology to explore sustainable models of eldercare services through an in-depth comparative analysis of care demands and service provision in two Chinese cities. The study reflects a prevailing trend of the integrated-care service mix in line with the United Nations’ five most relevant Sustainable Development Goals (SDGs 1, 3, 5, 10, 11) for older adults. In addition to the 7Ps of the service marketing mix, this article highlights the particular importance of ‘Partnership’ in sustainable care delivery in China. The past-present-future scenario and the thematic analysis of older adults’ pattern-matching add two unique dimensions to population ageing and eldercare studies: ‘People’ and ‘Partnership’.

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CARE: Context-awareness for elderly care

Cited by 4 publications

References 39 publications

Context-Aware Medical Systems within Healthcare Environments: A Systematic Scoping Review to Identify Subdomains and Significant Medical Contexts

Context-Aware Medical Systems within Healthcare Environments: A Systematic Scoping Review to Identify Subdomains and Significant Medical Contexts

Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Present and Future Trends of Sustainable Eldercare Services in China

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