Gait Disorder Classification Using CNN and TensorFlow Lite in Android Apps
Bibliographic record
Abstract
Gait disorders in older adults, especially those over 50, contribute to increased fall risk and reduced quality of life, making early detection essential.This study presents a deep learning-based approach for classifying gait patterns using vertical ground reaction force (vGRF) data.Signals from individuals with Parkinson's disease (PD) and healthy controls were pre-processed using band-pass filtering and wavelet denoising, then transformed into time-frequency spectrograms via Continuous Wavelet Transform (CWT).A Convolutional Neural Network (CNN) was trained on these spectrograms, achieving 93.48% accuracy with precision, recall, and F1-scores above 92%.The trained model was deployed in a TensorFlow Lite-powered mobile application, enabling real-time gait classification to support home-based monitoring and telemedicine.These findings highlight the potential of combining deep learning with mobile technology for accessible and automated gait disorder assessment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".