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Record W3116714578 · doi:10.1109/bibe50027.2020.00083

Frequency Response of a Novel IR Based Pressure Sensitive Mat for Well-being Assessment

2020· article· en· W3116714578 on OpenAlexafffund
Bruce Wallace, Julien Larivière-Chartier, Haoyang Liu, Tom Sloan, Rafik Goubran, Frank Knoefel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of OttawaBruyèreCarleton UniversityInnovation, Science and Economic Development Canada
FundersAGE-WELL
KeywordsFrequency responseResponse timeBandwidth (computing)Computer scienceDetectorMaterials scienceCommon emitterPressure sensorAcousticsElectronic engineeringOptoelectronicsEngineeringElectrical engineeringMechanical engineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

The potential for pressure sensitive mats to provide a means for ambient well-being sensing and assessment when placed within beds or chairs has been widely studied although many mats have limitations in capability and cost. This paper presents the results for a novel pressure sensitive mat that combines an Infrared (IR) based emitter/detector proximity sensor device with a flexible gel material (Hexyoo Scientific Inc. Opus Gel). The performance results for a bench prototype sensor that combines layers of gel that have differing colour and structure properties with the sensors is shown. Specifically, the step response of the prototype is analyzed for step-up and step-down loads representing a pressure range typical for human body pressures. The results show that the frequency response for the two cases are both low-pass in nature and that the frequency response for the step-down response is a loss of 10dB at 6Hz while the step-up response is narrower with a 10dB loss at 2Hz. This bandwidth is sufficient to allow assessment of human motions with the sensor, while also using significantly simpler and lower cost technology compared to previously reported fibre optic mats.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score0.694

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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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 designBench or experimental
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

Citations4
Published2020
Admission routes2
Has abstractyes

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