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Record W2806442490 · doi:10.11159/cdsr18.128

Evaluation of Walking Ability Using Variance Fractal Dimension Trajectory

2018· article· en· W2806442490 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

VenueProceedings of the International Conference of Control, Dynamic systems, and Robotics · 2018
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFractal dimensionTrajectoryDimension (graph theory)FractalVariance (accounting)Computer scienceArtificial intelligenceComputer visionMathematicsMathematical analysisPhysicsEconomics

Abstract

fetched live from OpenAlex

This paper describes a multiscale time-domain technique for evaluation of gait status of patients who are suffering from diseases such as stroke. This technique is based on variance fractal dimension trajectory (VFDT) algorithm that is applied to a shank acceleration signal. The signal is collected via an inertial measurement unit (IMU). However, its sampling frequency is not constant, and therefore interpolation is employed. Next frame size and step size are chosen properly to guarantee that the signals within all frames are stationarity. Next in order to avoid aliasing phenomenon, Nyquist theorem is checked. Finally VFDT is calculated and error is estimated. Results show that paralyzed legs have higher dimension values than healthy ones.

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)0.001
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 score0.885
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.056
GPT teacher head0.360
Teacher spread0.304 · 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