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[Application and Research of Digital Twin Technology in Safety and Health Monitoring of the Elderly in Community].

2019· article· zh· W2995214156 on OpenAlexaff
Jie Zhang, Hong Qian, Hongyuan Zhou

Bibliographic record

VenuePubMed · 2019
Typearticle
Languagezh
FieldComputer Science
TopicAdvanced Technologies in Various Fields
Canadian institutions123 Certification (Canada)
Fundersnot available
KeywordsALARMComputer scienceSafety monitoringCloud computingSet (abstract data type)Real-time computingProduct (mathematics)Artificial intelligenceEngineeringComputer securitySimulationElectrical engineeringOperating system

Abstract

fetched live from OpenAlex

In this paper, through the research of digital twin technology, combined with the application of vision sensor, artificial intelligence chip and deep learning algorithm technology, the real-time monitoring and alarm system of elderly fall and abnormal posture based on digital twin technology is developed. The system collects the data of the posture and behavior of the elderly, and then presents them in the cloud by digital mapping after the calculation and analysis of artificial intelligence chip. Once the safety threshold is deviated, the alarm can be activated to avoid or mitigate the injury caused by the fall of the elderly. Through product validation and trial operation of Tianbao Nursing Home in Hongkou District of Shanghai, the user can set alarm thresholds in different time periods and regions, thus achieving the preset purpose of the product.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.055
GPT teacher head0.333
Teacher spread0.278 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations11
Published2019
Admission routes1
Has abstractyes

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