MétaCan
Menu
Back to cohort
Record W2036495852 · doi:10.1109/iembs.2011.6091294

A new safety support system for wandering elderly persons

2011· article· en· W2036495852 on OpenAlexaff
Satoshi Matsuoka, Hayato Ogawa, Hiromichi Maki, Yoshiyuki Yonezawa, W.M. Caldwell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsTrinity College
Fundersnot available
KeywordsWearable computerMicrophonePresumptionComputer sciencePhoneMobile phoneThe InternetSound (geography)SoftwareComputer securityHuman–computer interactionInternet privacyMultimediaWorld Wide WebTelecommunicationsEmbedded systemOperating system

Abstract

fetched live from OpenAlex

We have developed a new mobile phone-based safety support system for transmitting information of a wandering elderly person's location and the environmental sounds around that person. The system consists of a wearable sensor and a conventional desktop PC with Internet access acting as the server computer. The wearable sensor, which is attached behind the neck of the elderly person's shirt, is composed of a low transmitting power mobile phone (W-SIM), a small microphone and a one chip microcontroller. The wandering elderly person's location is identified within 100 m from the mobile phone company's antenna ID via the W-SIM. The caregiver sets the elderly person's movement area by specialized computer software. When the elderly person goes out of the area, the sensor automatically records the environmental sound around the wandering elderly person for the presumption of the person's situation with the small microphone. The W-SIM sends both the wandering elderly person's location and the environmental sound to the server computer. The server computer informs automatically the caregiver by the e-mail. The caregiver can monitor the sound and the map of the wandering person's location via Internet. The sound enables the presumption of an accurate location and the situation of the wandering elderly person.

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: Methods · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.505

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 communication0.0000.001
Open science0.0010.000
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 head0.065
GPT teacher head0.247
Teacher spread0.181 · 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
GenreMethods

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

Citations19
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicContext-Aware Activity Recognition SystemsFrench-language works237,207