Advanced Exercise Classification with a Hybrid CNN-GRU Model: Utilising IMU Data from Cell Phones
Why this work is in the frame
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Bibliographic record
Abstract
We introduce a hybrid CNN-GRU model in this study to classify exercises using IMU time-series data, with a focus on jumping jacks, lunges, and squats.By combining Convolutional Neural Networks with Gated Recurrent Units, our model effectively manages the high dimensionality and variable sampling rates of IMU data.We employed data normalisation and augmentation techniques to refine the dataset.Our model showed high accuracy in classifying types of exercises, highlighting its potential in motion classification and fitness-tracking applications.These results emphasise the value of hybrid deep learning methods in analysing complex time-series data and make a significant contribution to the understanding of human exercise movement patterns.
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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 | 0.001 |
| Open science | 0.001 | 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