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Record W4226099297 · doi:10.47611/jsrhs.v10i4.1924

Exoskeleton for Knee Arthritis Patients

2021· article· en· W4226099297 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

VenueJournal of Student Research · 2021
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsExoskeletonSoftware portabilityTorqueSittingThighPhysical medicine and rehabilitationWearable computerArthritisPopulationMedicineBluetoothKnee JointComputer sciencePhysical therapySimulationWirelessSurgeryPhysicsTelecommunicationsEmbedded system

Abstract

fetched live from OpenAlex

Nearly 23 percent of the US adult population suffers from arthritis and patients of knee arthritis find it extremely painful to sit down or stand from a sitting position. In this research paper I have endeavoured to design a total knee support exoskeleton to assist people suffering from knee arthritis. High torque DC and servo motors were used in the model which can be controlled using a Bluetooth remote control or app. The choice of motors was based on the torque that is borne by the knee when the leg or thigh move. The model lends support to both the thigh and the lower leg and helps in motion of sitting, standing and lying down. The present model has been developed keeping in mind considerations of portability, affordability and commercial viability. The model has been conceptualized after making calculations on torque about the knee, and successfully reduces the metabolic cost of moving the leg or thigh. This exoskeleton will assist a wide population being lightweight, portable, and affordable and uses electrical parts to maximize the reduction in torque.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.169

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 head0.067
GPT teacher head0.385
Teacher spread0.318 · 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