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Record W4285689403 · doi:10.5383/juspn.12.02.004

iKarate: Karate Kata Aiding System

2020· article· en· W4285689403 on OpenAlexvenueno aff
Bassel Emad, Omar Atef, Yehya Shams, Ahmed El-Kerdany, Nada Shorim, Ayman Nabil, Ayman Atia

Bibliographic record

VenueJournal of Ubiquitous Systems and Pervasive Networks · 2020
Typearticle
Languageen
FieldComputer Science
TopicHuman Pose and Action Recognition
Canadian institutionsnot available
Fundersnot available
KeywordsMartial artsBlock (permutation group theory)Computer scienceArtificial intelligenceHuman–computer interactionVisual artsMathematicsArt

Abstract

fetched live from OpenAlex

Karate is a martial art that can be performed using hands and feet to deliver and block strikes. Karate Kata moves must be executed in a unique way, many moves are performed incorrectly during training. In this paper, we offer a system that Karate performers, coaches, judges and sporting clubs could use. The system aids the Karate performers by capturing their moves using Kinect v2 sensor, pre-processing these moves and then analyzing these moves using F-DTW. A report is displayed to the performers that is easily understandable, to learn how the mistakes were made, and how to fix it/learn from them. F-DTW was used for proving the concept, and an average accuracy of 93.65% was achieved. This Paper is mainly concerned about the first Karate Kata (Heian Shodan).

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.535

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 communication0.0000.001
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 head0.024
GPT teacher head0.217
Teacher spread0.193 · 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 designSimulation or modeling
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

Citations0
Published2020
Admission routes1
Has abstractyes

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