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A User-Driven Approach to Developing Ambient Assisted Living Systems for Older People

2010· book-chapter· en· W2490775179 on OpenAlexaff
Andrew Sixsmith, Sonja Mueller, Felicitas Lull, Michael Klein, Ilse Bierhoff, Sarah Delaney, Paula Byrne, Sandra Sproll, Robert Savage, Elena Avatangelou

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsUsabilitySoftware deploymentComputer scienceSet (abstract data type)Key (lock)VisualizationAssisted livingHuman–computer interactionField (mathematics)Older peopleRapid prototypingMultimediaProcess managementEngineeringSoftware engineeringMedicineGerontologyArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

This chapter describes some of the results of the EU-funded SOPRANO project to develop an Ambient Assisted Living system to promote active ageing and ageing-in-place. The chapter outlines SOPRANO‘s experience and application research approach to ensure that end-users are involved in all stages of the research and development. A number of key areas for application development were identified and developed as a set of use cases (or descriptive models), for example for medication reminding, and to support exercise. These use cases were further refined through visualization and iterative prototyping techniques with end-users to ensure usability, usefulness and acceptability for users. The SOPRANO prototype system is described together with future plans for deployment in demonstration sites and field trials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.004

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 head0.028
GPT teacher head0.280
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations15
Published2010
Admission routes1
Has abstractyes

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