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Record W2907014035 · doi:10.1109/newcas.2018.8585522

Evaluation of a Wearable and Wireless Human-Computer Interface Combining Head Motion and sEMG for People with Upper-Body Disabilities

2018· article· en· W2907014035 on OpenAlexaff
Cheikh Latyr Fall, Alexandre Campeau‐Lecours, Clément Gosselin, Benoit Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversité LavalPolytechnique Montréal
Fundersnot available
KeywordsWearable computerComputer scienceUsabilityInertial measurement unitInput deviceInterface (matter)WirelessGesture recognitionElectromyographyAccelerometerGestureHuman–computer interactionSimulationComputer visionComputer hardwareEmbedded systemPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

In this paper, a wearable, wireless and multimodal 2-dimensional computer mouse control system is introduced for people with upper-body disabilities. The proposed human-computer interface system combines body-motion, measured using inertial measurement unit (IMU), to provide a cursor velocity and displacement control, and surface electromyography (sEMG) for target selection (left-click), using custom sensors made of electronic components of the shelf. Its functionality is demonstrated by using head motion and muscular activity detection from trapeze muscles to evaluate usability by people living with severe disabilities, congenital absence or amputation of upper-members, temporary limb traumatism, etc., preventing their utilization of tools such as mouse or keyboard. Performance using different control topologies, following the ISO/TS 9241-411:2012 standard encompassing the evaluation of physical pointing tasks, and compared to a computer mouse. On average, over the 3 participants, results show that the proposed interface can provide an index of performance of 0.18 bits/s versus 2.2 bits/s with a mouse pointer. Head motion combined with sEMG showed a 4% accuracy drop computer to the mouse while being more suitable for the severely disabled.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.284

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 head0.034
GPT teacher head0.313
Teacher spread0.279 · 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 designSimulation or modeling
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

Citations5
Published2018
Admission routes1
Has abstractyes

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