TEMPORAL FEATURES ESTIMATED FROM ELETROMYOGRAPHIC AND INERTIAL SIGNALS FOR THE ASSESSMENT OF BRADYKINESIA IN PEOPLE WITH PARKINSON'S DISEASE
Bibliographic record
Abstract
Several motor disorders can cause the slowness of voluntary movements, known as bradykinesia. For instance, in people with Parkinson’s disease (PD) the evaluation of bradykinesia together with other cardinal signs are of great relevance for the diagnosis and follow-up of the disorder. In this context, the development of methods for the objective assessment of bradykinesia is relevant. This research proposes the use of temporal features extracted from electromyographic and inertial signals for the evaluation of bradykinesia. Data were collected from six people with Parkinson’s disease who executed pronation/supination and flexion/extension of the hand, and pinch movement. The overall mean time of distinct features were estimated for each participant, characterizing, thus, typical values of the features for the studied sample. The participants were ranked according to their mean temporal features, illustrating that this parameter can be used as an objective measure for the evaluation of bradykinesia. In the future, the proposed method should be applied to a larger group of people with Parkinson’s disease and a healthy control group so that it is possible to characterize the temporal features adequately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".