MétaCan
Menu
Back to cohort
Record W1992034376 · doi:10.1109/biorob.2012.6290674

Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface

2012· article· en· W1992034376 on OpenAlexaff
Jacob Webb, Zhen Gang Xiao, Katharina P. Aschenbrenner, Gil Herrnstadt, Carlo Menon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsExoskeletonBrain–computer interfaceRehabilitationInterface (matter)Computer scienceLimitingAssistive deviceAssistive technologyHuman–computer interactionPhysical medicine and rehabilitationStroke (engine)Robotic armSimulationEngineeringMedicineArtificial intelligencePhysical therapyOperating system

Abstract

fetched live from OpenAlex

Recent research has shown the benefits of using robotic devices to aid in stroke patient rehabilitation. In addition, the use of brain computer interfaces is showing promising applications in both controlling robotic devices and aiding in neural rehabilitation. Unfortunately, traditional rehabilitative devices are cumbersome and are not being used outside the laboratory environment, therefore potentially limiting patient access to these new rehabilitative technologies. This project seeks to address this issue by working toward creating a portable rehabilitative system consisting of an arm exoskeleton controlled by a brain computer interface (BCI). A wireless commercially available BCI system was used (Emotiv EPOC) to actuate the device in a single direction. A pilot study was conducted to evaluate the performance of the system on four healthy adult volunteers. Results show all users were able to control the device with success rates above chance.

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)0.001
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 score0.351
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.302
Teacher spread0.276 · 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

Citations26
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicEEG and Brain-Computer InterfacesFrench-language works237,207