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Record W2101518335 · doi:10.1109/pacrim.2007.4313313

A Comparative Study on Wearable Sensors for Signal Processing on the North Indian Tabla

2007· article· en· W2101518335 on OpenAlex
Manjinder Singh Benning, Ajay Kapur, Bernie C. Till, George Tzanetakis, Peter F. Driessen

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
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWearable computerComputer scienceAccelerometerGestureInertial measurement unitWirelessSignal processingWearable technologyArtificial intelligenceComputer visionSIGNAL (programming language)Computer hardwareEmbedded systemDigital signal processingTelecommunications

Abstract

fetched live from OpenAlex

This paper describes experimentation using a variety of sensor techniques to capture body gestures and train a student performing the North Indian hand drums known as the Tabla. A comparative study of motion capture systems, wearable accelerometer units, and wireless inertial sensor packages, is described. Each acquisition method has it advantages and disadvantages which are explored through trial and error. The paper describes a number of applications using real-time signal processing techniques for analysis, performance, performer posture detection and machine perception of human interaction.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.063
GPT teacher head0.310
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

Citations5
Published2007
Admission routes1
Has abstractyes

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