Dispositivo portátil para análise cinemática da marcha de indivíduos pós Acidente Vascular Cerebral em ambiente ambulatorial: um estudo prova de conceito
Bibliographic record
Abstract
Introduction: Wearable sensors have been used in the kinematic analysis of hemiparetic gait, proving to be a more accessible and simpler alternative to use. Before this technology reaches the market, it is necessary to conduct a usability study of this device. Objective: This study aimed to verify the feasibility and preliminary effectiveness of a portable device for analyzing kinematic ga parameters in post-stroke individuals in an outpatient setting. Methods: This proof-of-concept study tested a device called DAMA, consisting of two inertial sensors used to measure ankle angular parameters during gait. The testing was conducted on 5 individuals without stroke and 5 post-stroke individuals, using the Qualisys Motion Capture System® as the reference measure. During device testing, feasibility was assessed based on outcomes related to the device's operational usability (using a custom questionnaire) and user satisfaction (using the Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 - Brazilian version questionnaire). The Mann-Whitney test was used to compare the results of the two groups regarding operational usability and user satisfaction outcomes. The Wilcoxon test was used to compare the ankle angular measurements of the DAMA device and the Qualisys®. Results: The majority of participants were adult men (mean age 59 years). The operational usability and user satisfaction of the device were considered satisfactory for both groups (operational usability: post-stroke: 4.5 - without stroke: 4.12; user satisfaction: post-stroke: 4.45 - without stroke: 4.57). In the angular data, initial contact (p=0.006), peak dorsiflexion (p<0.001), and pe plantar flexion (p<0.001) were different between DAMA and Qualisys® in post-stroke group; peak plantar flexion (p<0.001) was also different in the g without stroke. Conclusion: The device proved to be feasible due to the satisfactory indices of operational usability and user satisfaction. In a preliminary effic analysis, some ankle angular data showed discrepancies between the devices, indicating the need for possible adjustments in this regard.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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.
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".