MétaCan
Menu
Back to cohort
Record W4367277951 · doi:10.1123/jab.2022-0249

The Effect of Sensor Placement on Measured Distal Tibial Accelerations During Running

2023· article· en· W4367277951 on OpenAlexaff
Lauren K. Sara, Jereme Outerleys, Caleb D. Johnson

Bibliographic record

VenueJournal of Applied Biomechanics · 2023
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsInertial measurement unitGround reaction forceStandard deviationAccelerometerUnits of measurementGeodesyMathematicsMedial malleolusOrthodonticsComputer scienceGeologyMedicinePhysicsKinematicsSurgeryStatisticsArtificial intelligenceAnkle

Abstract

fetched live from OpenAlex

Inertial measurement units (IMUs) attached to the distal tibia are a validated method of measuring lower-extremity impact accelerations, called tibial accelerations (TAs), in runners. However, no studies have investigated the effects of small errors in IMU placement, which would be expected in real-world, autonomous use of IMUs. The purpose of this study was to evaluate the effect of a small proximal shift in IMU location on mean TAs and relationships between TAs and ground reaction force loading rates. IMUs were strapped to 18 injury-free runners at a specified standard location (∼1 cm proximal to medial malleolus) and 2 cm proximal to the standard location. TAs and ground reaction forces were measured while participants ran at self-selected and 10% slower/faster speeds. Mean TA was lower at the standard versus proximal IMU location in the faster running condition (P = .026), but similar in the slower (P = .643) and self-selected conditions (P = .654). Mean TAs measured at the standard IMU explained more variation in ground reaction force loading rates (r2 = .79-.90; P < .001) compared with those measured at the proximal IMU (r2 = .65-.72; P < .001). These results suggest that careful attention should be given to IMU placement when measuring TAs during running.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.445

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 head0.013
GPT teacher head0.224
Teacher spread0.210 · 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 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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied BiomechanicsSame topicLower Extremity Biomechanics and PathologiesFrench-language works237,207