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2P1-B09 Recognition of human body movement using Inertial Measurement Unit(Sense, Motion and Measurement (3))

2012· article· en· W2652186651 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

VenueThe Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicAdvanced Computing and Algorithms
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSegmentationArtificial intelligenceInertial measurement unitComputer visionMovement (music)Computer scienceInertial frame of referenceMotion (physics)Motion capturePattern recognition (psychology)AcousticsPhysics

Abstract

fetched live from OpenAlex

The objective of this study is the realization of recognition of human body movement using IMU (Inertial Measurement Unit). This paper shows segmentation and the recognition results of the movement of the right arm during Japanese drum performance to confirm the segmentation and recognition possibilities. In past studies the real-time recognition of human body movement using joint angles acquired by motion capture as been realized. The program of segmentation for joint angles is appropriately modified for angular velocity. And to adjust the parameter settings in order to match the segmentation results in manual segmentation results. The similar movement patterns are classified into each model. Recognition is performed using these models.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.122
GPT teacher head0.317
Teacher spread0.195 · 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