A Mobile Assistive Application for People with Cognitive Decline

Xenakidis, Christos N.; Hadjiantonis, A.; Milis, George

doi:10.1109/itag.2014.18

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…These studies were also related, as the COGKNOW solution was integrated in the Rosetta solution. Two studies, AP@LZ (Imbeault et al, 2014), and Alzminder (Xenakidis et al, 2014), presented holistic app solutions comprising a variety of features that could support the user in everyday life, e.g. reminders, information on contacts, medication information, or emergency contacts.…”

Section: Resultsmentioning

confidence: 99%

“…However, Donnelly et al involved a small group of end users in a prototype pretest. Xenakidis et al (2014) designed their prototype app based on the analysis of already existing technology, and they consulted stakeholders and people with mild cognitive disorder. They did not report any specific details of this involvement or indicate if it had any impact on the design of their app.…”

Section: Resultsmentioning

confidence: 99%

“…Two studies (Robinson et al, 2009;Xenakidis et al, 2014) did not include a specific test phase in their publications, but Robinson et al (2009) included feedback from two end users and one family caregiver during the design of their prototypes. The Alzminder app (Xenakidis et al, 2014) has been made available as open access on Google Play (2019) for prototype testing. We did not locate any scientific or other publications related to this test phase.…”

Section: Resultsmentioning

confidence: 99%

“…One study (Boyd et al, 2017) only addressed caregivers with training sessions and telephone support. In addition, Xenakidis et al (2014) included audio-based guidance as part of their design of the app.…”

Section: Review Aim 3: Dissemination and Adoption Of At For People Wimentioning

confidence: 99%

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Assistive technology designed to support self-management of people with dementia: user involvement, dissemination, and adoption. A scoping review

Øksnebjerg

Janbek

Woods

et al. 2019

Int. Psychogeriatr.

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Background: Assistive technology is advocated as a key solution to the need for support among people living with dementia. There is growing awareness of the benefits of user involvement in the design and test of these technologies and the need to identifying applicable and effective methods for implementation. The aim of this review was to explore and synthesize research addressing assistive technology designed to be used by people with dementia for self-management. Further research aims were to explore if and how user involvement, dissemination, and adoption of assistive technology were addressed.Method: Electronic databases were searched using specified search terms. Key publications and grey literature sources were hand-searched. Materials published until year end 2018 were included. The results were summarized according to the research aims.Results: Eleven papers derived from eight studies were included. The studies presented data from prototype design and testing, and the review showed great variation in study scope, design, and methodology. User involvement varied from extensive involvement to no user involvement. Methods for adoption also varied widely and only targeted prototype testing. None of the studies addressed dissemination. Conclusion:The results of this review underline the need for well-designed high-quality research into all the aspects that are essential to deliver applicable, effective, and sustainable assistive technology to support self-management of people with dementia. There is a need for evidence-based methods to promote and qualify user involvement, dissemination, and adoption. The results also point to the need for standardized outcome measures and standards for conducting and reporting research to improve its quality and impact.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Review Aim 3: Dissemination and Adoption Of At For People Wimentioning

confidence: 99%

See 2 more Smart Citations

Assistive technology designed to support self-management of people with dementia: user involvement, dissemination, and adoption. A scoping review

Øksnebjerg

Janbek

Woods

et al. 2019

Int. Psychogeriatr.

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“…In the health monitoring context, human-machine interaction is intended to identify new ways in which existing knowledge from the fields of medicine, nursing, psychology, cognitive science and computer science can be combined and used to support people with cognitive-related diseases. Existing assistive tools for that aim have been analysed in [138] (devices can be noted on 2.4) concluding that these tools may be replaced by mobile applications combined in a single suite of applications. The most relevant needs identified are the following ones:…”

Section: Human-machinementioning

confidence: 99%

Modeling human behaviour to improve retail efficiency and health monitoring

González¹

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This dissertation presents a model to express the most relevant features of the human behaviour, specifying two personalized instances for retail and health Monitoring domains. These features are divided into static and dynamic. For the static ones facial features are the most relevant since they can express different attributes of the subjects such as age, gender, emotions ... For that purpose landmarks and pose are the most significant points of interest. However, the body pose can also reveal some interesting features of the current state of one person and should also be taken into account. Regarding the dynamic properties, the trajectories described by a subject in the monitored scenario can also express very useful insights. It is a basic task to delimit the area where the subject is located that is mandatory to perform the static inferences. The interactions of the user with the environment are a very relevant feature, and should be modeled as well. Thus, initial model has been evolved for the domains previously mentioned. Retail is a scenario where the shoppers express a large amount of features that should be considered by the retailers. Concepts such as density of the areas of the establishment, focus of attention, interactions with the shelves, queues management or characterization of the visitors are included in the model to improve the efficiency of the physical shops. A completely different field such as health monitoring could lead to a completely different approach. But the trajectories of the patients, their facial properties, their interactions or their body pose are also meaningful in this domain to assist decision-making of the health professionals. In our work we have mainly studied the behaviour of the patients with cognitive diseases. This field requires monitoring in different scenarios such as the home of the patient, the clinic centre or the rehabilitation centre. A scalabe system and its deployment is proposed to provide an accurate monitoring. In this area we shall also include medical features such as the treatment of the patients, their body pose evolution in physiotherapy sessions or measurements such as their body temperature or their heart rate. Stated the architecture of every domain, we have gone beyond in two Computer Vision and Machine Learning tasks: human pedestrian tracking and facial analysis. For the first we have developed two solutions that have been tested on real scenarios and on controlled environments that are suitable for a deeper analysis. Feature descriptors such as HOG or LBP have been tested, and Machine Learning classifiers such as Adaboost or Support Vector Machines have been optimized. We propose the best combination for previously exposed techniques and we show results on the different scenarios proposed to validate our proposal. A 3D visual imaging system is optimized by the proposal of a multi-camera RGB-D system able to capture facial properties at extreme poses. The data collection gathered allow 3D reconstruction of facial areas of 92 subjects captured,...

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