>i<Smartvest>/i<: tecnologia assistiva para percepção e correção postural de pessoas com acidente vascular cerebral
Bibliographic record
Abstract
Individuals with functional limitations due to Stroke have short-and long-term difficulties in perception and postural control.The purpose of this study is to describe the development of assistive technology (AT), which assists in postural perception and control, for people with hemiparesis and rehabilitation professionals.The methodology of products was used in the research, the needs of groups were transformed into technology requirements, model generation, optimizations and tests in the target populations were performed.The AT called: Smartvest, is a smartphone application and a dress that allows the calibration of postures of the trunk and sends corrective signals.Smartvest was developed in partnership with the Institute of Computer Science and Computational Mathematics of the University of São Paulo (ICMC / USP).The users' satisfaction was analyzed by the Quebec User Evaluation of Satisfaction withAssistive Technology (QUEST 2.0) in an adapted way, the perception of the trunk movement amplitude (flexion / extension, rotation and lateral flexion) by the AT was compared to a kinematic analysis device and a study of the therapeutic viability in the rehabilitation process was conducted.The results obtained demonstrate that the technology in kinematic analysis by the Vicon® Gait Plug-in system represents the trunk ADM with differences smaller than 10º (degrees).Smartvest was considered a viable resource for the rehabilitation and facilitation goals of therapists (100%), followed principles of comfort (65%), ease of use (60%) and effectiveness (45%) for people with stroke, in daily activities of rehabilitation, it was observed that people with stroke receiving the guidelines of AT achieved 3.13 times more correct postures with fewer trunk displacement errors (1.75 times).Smartvest presented potential therapeutic use in rehabilitation as a facilitator of perception of posture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".