Inertial Sensor Dynamics, Selection and Applications for Epileptic Seizure Detection
Bibliographic record
Abstract
In this work, an optimal placement strategy for wearable inertial sensor placement on a human arm is presented with an ultimate goal of using a sensor cluster for epileptic seizure detection. The present study shows that the placement of sensors plays a significant role in achieving high sensing resolution. Employing the commercial package Motion Genesis™, a procedure is first developed to simulate the dynamic response when human body is under typical epileptic seizure episodes. The method uses Kane’s method to obtain equations of motion and the resulting equations of motion are numerically solved using Matlab™ when the system is subjected to a prescribed seizure input. Simulation results give insights into influences of the neurological disorder on sensing in quantitative terms. Based on certain assumptions on the predicted quantitative measures of the response, optimal detection performance based on sensor location and number of sensors is proposed with particular emphasis on sensor resolution and noise. The optimization is carried out employing the genetic algorithm module of Matlab global optimization toolbox to find the optimal placement of sensors to achieve least sensing noise in calculating the angular acceleration. The results are also verified with those predicted via simulated annealing. The predictions have also been validated via suitable sensitivity analysis to evaluate the efficacy of the method to uncertainties in the biomechanical, geometry as well as sensor noise parameters. The proposed optimal placement predictions are envisaged to be instrumental for the implementation of a wearable inertial sensor cluster.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".