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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.055
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