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A Joint Localization Assisted Respiratory Rate Estimation using IR-UWB Radars

2020· article· en· W3082231457 on OpenAlex
Shan He, Varun Mehta, Miodrag Bolić

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceBreathingRadarRespiratory rateRadar trackerComputer visionTrack (disk drive)Impulse (physics)Artificial intelligenceRespiratory monitoringJoint (building)Remote sensingSimulationReal-time computingTelecommunicationsEngineeringRespiratory systemGeologyMedicinePhysicsHeart rate

Abstract

fetched live from OpenAlex

Respiratory rate (RR) is one of the vital signs which is commonly measured by contact-based methods, such as using a breathing belt. Recently, significant research has been conducted related to contactless RR monitoring - however, the majority of experiments are performed in situations when the subject is oriented towards the radar. In this research, we are interested in monitoring the breathing of subjects who can be anywhere in the room. A system of three impulse radio ultrawideband (IR-UWB) radars is used to cover the whole room. A Kinect camera that can track subjects' joints 3D coordinates was employed to localize the subjects. The results of RR monitoring using IR-UWB radars and Kinect camera show good performance in single/multiple subject(s) tracking and RR estimation.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.575

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 communication0.0000.000
Open science0.0000.000
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 head0.064
GPT teacher head0.250
Teacher spread0.186 · 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

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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