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Record W3113519935 · doi:10.1109/tmech.2021.3049327

Characterization of a Packaged Triboelectric Harvester Under Simulated Gait Loading for Total Knee Replacement

2021· article· en· W3113519935 on OpenAlexaff
Nabid Aunjum Hossain, Geofrey Yamomo, Ryan Willing, Shahrzad Towfighian

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsWestern University
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsTriboelectric effectGaitCharacterization (materials science)Total knee replacementMaterials scienceAutomotive engineeringPhysical medicine and rehabilitationComposite materialMedicineEngineeringNanotechnologySurgery

Abstract

fetched live from OpenAlex

Load sensing total knee replacement (TKR) implants are useful tools for monitoring prosthesis health and providing quantitative data to support patient claims of pain or instability. However, powering such devices throughout the entire life of the knee replacement is a challenge, and self-powered telemetry <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">via</i> energy harvesting is an attractive solution. In this article, we implemented vertical contact mode triboelectric energy harvesters inside a knee implant package to generate the power required for embedded digitization and communications circuitry. The harvesters produce small-scale electric power from physiologically relevant loads transmitted through the knee. Experiments were performed on a joint motion simulator with an instrumented package prototype between the polyethylene bearing and tibial tray. The amplitude and the pattern of the power output varied with the input loadings. Under sinusoidal loading the maximum apparent power harvested was around 7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> W at (50–2000)N whereas, under vertical compressive gait loading the harvesters generated around 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> W at average human knee loads of (151–1950)N and 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> W when the maximum applied load was increased by 25%. Full six degrees-of-freedom gait load/motions at 0.67 Hz produced 50% less power due to the slower loading rate. The results show the potential of developing a triboelectric energy harvesting-based self-powered instrumented knee implant for long-term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> knee joint force measurement.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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

Citations28
Published2021
Admission routes1
Has abstractyes

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