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Record W7038233838

Haptically-enabled devices for neurological rehabilitation of hand and wrist disabilities

2022· dissertation· en· W7038233838 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationSet (abstract data type)Neurological rehabilitationNeurorehabilitationHaptic technologyInterface (matter)Variety (cybernetics)Wrist
DOInot available

Abstract

fetched live from OpenAlex

Successful rehabilitation of individuals impaired by neurological disabilities is a critical endeavor as patients will otherwise be unable to care for themselves. This situation results in decreases in both functional independence and overall quality of life for the individual, which in turn can cause negative emotional effects. As with all rehabilitative therapies, a variety of safe, appropriate, stimulating, and relevant activities must be available, otherwise patients will become disinterested and therefore non-compliant. The development of interactive programs utilizing commercially available haptic equipment will allow for the introduction of a novel set of rehabilitative exercises which will improve upper extremity fine motor skills. The final developed products will additionally facilitate precise evaluation and monitoring of the patients’ functions and progress as well as providing a more stimulating environment for the patient. This will allow for deficiencies to be detected promptly and subsequently precisely addressed as well as providing valuable motivation to the patients. This thesis’s intention was to develop novel robotically manipulandum enhanced equipment to aid in neurological diagnostic and rehabilitative exercises. The developed systems consist of two separate apparatuses, each with their own unique set of software tools and accompanying hardware. The first device is an extension to a pre-existing game developed on behalf of the College of Rehabilitation Sciences of the University of Manitoba for both diagnostic and rehabilitative neurological exercises. This tool acts as an interactive robotic interface for this currently utilized game, allowing for additional benefits, such as adding or removing additional difficulty to the gameplay. This increases overall patient engagement with the exercise in question. The second device is a stand-alone system allowing for tracing exercises to be undertaken in a robotically enhanced environment. This device allows for patients to practice the fine motor skills associated with writing without any external human assistance, while simultaneously aiding in the recovery or development of general fine motor skills. The necessary hardware for this robotically enhanced manipulandum tracing computer program consists of a portable lightweight passive fixture into which an unmodified and fully functional commercial haptic device can be temporary installed when desired.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.202
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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