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Record W1552405914 · doi:10.1109/icassp.2015.7178283

Posture-invariant ECG recognition with posture detection

2015· article· en· W1552405914 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

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiometricsComputer scienceBody postureArtificial intelligenceComputer visionModality (human–computer interaction)Context (archaeology)Invariant (physics)Pattern recognition (psychology)Speech recognitionMathematicsPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Recently Electrocardiogram (ECG) has been proposed as a biometric modality which offers liveliness detection. The fact that ECG is a vital signal makes it challenging to work with as it is affected by physical and psychological changes. In realistic applications, this type of biometrics still needs to be verified in conditions related to the practical use. In real life our body posture changes frequently, therefore in the context of a biometric system our body posture may be different in enrolment and verification which can potentially decrease the performance of the system. In this paper we first investigate the effect of the body posture on the accuracy of ECG biometric systems. Second, a new method is presented that is able to clearly distinguish the ECG signal of different postures of an individual. Finally, we propose a posture-detection verification system in order to mitigate the effect of body posture by first detecting the posture of a subject and then identifying it.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.230

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 head0.033
GPT teacher head0.257
Teacher spread0.223 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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