Avaliação de usabilidade, desempenho ocupacional e satisfação com sistema de controle de ambiente inteligente por pessoas com deficiência motora severa através de eletromiografia de superfície e oculografia por infravermelho
Bibliographic record
Abstract
A smart environment (SE) is an Assistive Technology (AT) resource that allows people with motor disabilities, even with low mobility, to control the lighting and electronic equipment (TV, radio, fan, etc) of the environment through a Human-Machine Interface (HMI) configured to be activated by biomedical signals. However, despite the recognized importance, the AT resource is not always considered useful, reaching high abandonment rates, since a prior assessment and prescription by professionals is necessary, taking into account the real demands and needs of the person with disability. This Doctoral Thesis aims to evaluate the effectiveness of a smart environment system controlled by surface electromyography and by infrared oculography, captured by an eye tracker, used by people with motor disabilities. Six volunteers participated in the research, and, initially, were applied socio-demographic data forms, Functional Independence Measure (FIM TM) and Canadian Occupational Performance Measure (COPM). The subjects were presented to the equipment and system interface, being trained for their use in domestic environment, using the system for a week. Afterwards, they were re-evaluated with the COPM, besides evaluations of satisfaction with the use of the AT resource (form B-QUEST 2.0), psychosocial impact (form PIADS), usability of the system (SUS form) and semi-structured interview for suggestions or complaints. The control of TV was the common demand of all participants. As a result of this research, of the six volunteers, four used the system, presenting positive results regarding the change in occupational performance, satisfaction with performance and the smart environment system, high psychosocial impact and good system usability. It was evaluated that the developed system also provided greater independence of the volunteers for the control of the equipment. Regarding the volunteers who did not use the system, aspects such as nonacceptance of the disability and lack of social support may have influenced. The SE system proved to be effective, improving all aspects evaluated in the participants. A patent application of the developed system was submitted to INIT-UFES. Future studies should consider expanding the possibilities of controlled equipment and devices, as well as the time of use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".