Gait Pattern Analysis for Early Detection of Neuromuscular Disorders Using Wearable Sensors and Artificial Intelligence Techniques
Bibliographic record
Abstract
In this work, a gait-based approach for early detection of cerebellar ataxia is introduced using deep learning and gait data collected from an array of wearable knock sensors mounted on the legs.The dataset was collected from the Kaggle Gait Analysis Dataset for cerebellar ataxia and pre-processed by z-score normalization and segmented into 128 samples with 50% overlap.A customized Convolutional Neural Network (CNN) model was designed and trained on these segments to classify gait patterns as normal and ataxic.The training mechanism indicated that the accuracy quickly grew to 95%, close to 100% at the end of the training.However, the trained CNN provided only a moderate accuracy of 40%, with a precision of 0.57 and a recall of 0.31 for normal gait and a precision of 0.31 and a recall of 0.57 for ataxic gait, thus resulting in F1-scores of 0.4 for both classes on unseen test data.The confusion matrix reflected an imbalance of ergonomics towards overprediction of ataxia by a nine-over-thirteen number of normal 'samples' misclassified.Whereas the model provides high confidence prediction scores (81%-99%) even for misclassifications, this indicates a model prone to overfitting and lacking generalizability.These findings underscore the promise and challenges of AI-augmented gait diagnostics, with model calibration, dataset balancing, and feature refinement indicated to improve sensitivity and specificity for clinical utility.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".