Characterization of Upper-Limb Pathological Tremors: Application to Design of an Augmented Haptic Rehabilitation System
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an adaptive filtering technique is proposed to estimate and characterize pathological tremors caused by Parkinson's Disease (PD) and Essential Tremor (ET). The technique is based on the formulation of band-limited multiple Fourier Linear Combiners (BMFLC) and is called Enhanced-BMFLC (E-BMFLC). The effectiveness of the designed filter is statistically evaluated through a clinical study involving 14 PD and 13 ET patients. The hand tremors of the participants are studied in three Degrees Of Freedom (DOF). Using statistical analysis, it is shown that the new design of the filter significantly enhances the accuracy in comparison with the performance of conventional BMFLC filtering. In addition, E-BMFLC significantly reduces the sensitivity to parameter tuning and intrapatient variabilities. The observed improvements are achieved by modulating the memory of the proposed filter, and by enriching the utilized harmonic model. The proposed filter is then used to develop a safe haptics-enabled robotic rehabilitation architecture, designed for patients having hand tremors. The architecture is entitled Augmented Haptic Rehabilitation (AHR), which enables adaptive management of the involuntary components of the hand motion while delivering assist-as-needed haptic therapy (for the voluntary component) and avoiding unsafe amplification of hand tremors. Experimental evaluations are provided to evaluate the efficacy of the proposed AHR system.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it