Ubiquitous computing for health applications

Baños, Oresti; Hervás, Ramón

doi:10.1007/s12652-018-0875-3

Cited by 2 publications

(1 citation statement)

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“…Ubiquitous computing (synonym of pervasive computing) has found applications in wide knowledge areas such as, health, commerce, logistics and games (Banos and Hervás, 2019; Franco et al , 2011; Marinagi et al , 2013; Hinske et al , 2007). There is a great need of real-time data collection and its analysis for these applications.…”

Section: Introductionmentioning

confidence: 99%

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

Goel

Tushir²

2021

IJPCC

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Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

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Section: Introductionmentioning

confidence: 99%

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

Goel

Tushir²

2021

IJPCC

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External ambient cues to mediate the involvement of family caregivers in the management of older adults’ medications: a qualitative study (Preprint)

Rodríguez¹,

García-Vázquez²,

Andrade³

2019

Preprint

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BACKGROUND The most common reasons for the lack of adherence among older adults is the forgetfulness due to the multiple cognitive processes associated with prospective memory involved in remembering to follow a medication regimen. Problems with prospective memory increase the demands for care. Therefore, family members play an essential role in supporting patients in activities that involve: helping with medication adherence, transmitting knowledge about medications, and helping to refill their prescriptions. To address some of these medication problems, we designed a Medication Ambient Display (MAD) to support education, motivation, and measurement of medication-taking behaviors in older adults. The functionality of MAD consists of providing external cues to convey information through abstract and stylized representations of older adults’ medication adherence. Specifically, MAD is placed as a portrait in the elderly’s home. It shows a virtual birdcage to raise elders’ consciousness about their responsibility for caring for their health, in a similar way that they gladly take care of their pets OBJECTIVE This study aimed to assess the effect of MAD’s external cues on the involvement of family members in the management of medications for the elderly. METHODS We conducted a qualitative study consisting of semi-structured interviews with older adults and their relatives identified as informal caregivers. The study lasted ten (10) weeks. We visited participants weekly to collect data about: older adults’ medication problems, help received, and their perception about older adults’ medication adherence. The first five (5) weeks of the study was used to establish baseline data to understand the involvement of relatives on medication management activities. Afterward, we introduced MAD to be used for the successive five (5) weeks. RESULTS We found that new patterns of interaction among older adults and their relatives arose while using MAD. Participants perceived that MAD reinforced the care functions of family caregivers. For instance, it made them aware of older adults' medication adherence and encouraged youngest family members to help older adults. CONCLUSIONS We identified that the external cues provided through ambient displays did not overwhelm family members, but motivated their social connectedness with older adults. We conclude this paper with a set of lessons learned from this qualitative assessment, which may help designers to develop assistive technology for older adults.

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Ubiquitous computing for health applications

Cited by 2 publications

References 10 publications

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

External ambient cues to mediate the involvement of family caregivers in the management of older adults’ medications: a qualitative study (Preprint)

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