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Record W2758375253 · doi:10.1109/iscas.2017.8050899

Ultrasound sensors and its application in human heart rate monitoring

2017· article· en· W2758375253 on OpenAlex
Amirhossein Shahshahani, Davood Raeisi Nafchi, Željko Žilić

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 institutionsMcGill University
Fundersnot available
KeywordsUltrasoundComputer scienceUltrasonic imagingMedicineRadiology

Abstract

fetched live from OpenAlex

Noninvasive wearable human health monitoring devices are developed to improve the comfort, convenience, and security of humans in their life. Ultrasound technology has been used for imaging the human body for over half a century. In this study, the use of ultrasound as a wearable device for human health monitoring is introduced. This work investigates analysis of the heart motions for heart rate extraction. Experimental results showed promising performance of the proposed method in reference to an electrocardiogram device. A low power and low complexity hardware prototype is designed to measure the Time Of Flight (TOF) and amplitude of reflected ultrasound signals generated by piezo sensors at 1 MHz (nominally), under design considerations for safety issues of intensity exposure defined by FDA. This type of a wearable human-interactive device represents a promising platform not only for heart rate measurement but also for more feasible features such as respiration rate. A new technique is applied to minimize the signal processing time and ensure the device response correctness.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.484

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 head0.020
GPT teacher head0.267
Teacher spread0.248 · 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
Published2017
Admission routes1
Has abstractyes

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