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Privacy-Aware and Acceptable Lifelogging services for older and frail people: the PAAL project

2018· article· en· W2904800513 on OpenAlexaff
Francisco Flórez‐Revuelta, Alex Mihailidis, Martina Ziefle, Liane Colonna, Susanna Spinsante

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLifelogInternet privacyContext (archaeology)Variety (cybernetics)Order (exchange)Assisted livingBusinessInformation privacyIndependence (probability theory)Computer scienceGerontologyMedicine

Abstract

fetched live from OpenAlex

Developed countries around the world are facing crucial challenges regarding health and social care because of the demographic change and current economic context. Innovation in technologies and services for Active and Assisted Living stands out as a promising solution to address these challenges, while profiting from the economic opportunities. For instance, lifelogging technologies may enable and motivate individuals to pervasively capture data about them, their environment and the people with whom they interact, in order to receive a variety of services to increase their health, well-being and independence. In this context, the PAAL project presented in this paper has been conceived, with a manifold aim: to increase the awareness about the ethical, legal, social and privacy issues associated to lifelogging technologies; to propose privacy-aware lifelogging services for older people, evaluating their acceptability issues and barriers to familiarity with technology; to develop specific applications referred to relevant use cases for older and frail people.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.283
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2018
Admission routes1
Has abstractyes

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