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Record W6989489728

Benefit of "rysis" : A Wheelchair Seated Posture Measurement Based on ISO 16840-1

2016· article· W6989489728 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2016
Typearticle
Language
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsnot available
Fundersnot available
KeywordsWheelchairManual wheelchairUnits of measurementSoftwareWork (physics)International standard
DOInot available

Abstract

fetched live from OpenAlex

This article is of a measurement method of wheelchair seated posture. First, we have reported a brief outline of ISO 16840-1 world standard regarding posture while seated in a wheelchair; the standard specifies a global coordinate system. We also introduced measurement tools based on ISO 16840 and the current Japanese clinical works using the tools. Secondly, we have reported how to use "rysis", the ISO standard-based wheelchair seated posture measurement (WSPM) software, which was invented by one of the authors Takashi Handa innovated in 2008. Since 2009, we have been promoting clinical application of "rysis". As of June 2014, the number of distribution facilities of the "rysis" was over 300 in the world. Thirdly, we have reported how to conduct a survey using "rysis" and have stated the results of the research. We have presented our research booth during the Barrie Free Trade Show (BF), which has annually held with almost one hundred thousand visitors at Intec Osaka, Osaka, Japan. From BF visitors in the past six years, a total of 266 daily wheelchair users participated in the research. Our measurement results can directly visualize their wheelchair seated posture. We have been developing skills and knowledge for clinical application and improving the software usability, Finally, we concluded the benefit of "rysis" that include empowerment of people with disability and the educational impact on both consumers and students.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.026
GPT teacher head0.242
Teacher spread0.216 · 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.

Study designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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