Deep Learning Ensemble for Recognising Lower Limb Activity
Bibliographic record
Abstract
Security, healthcare, elderly care, rehabilitation, and sports science are just a few of the areas that can benefit from the analysis of lower limb motion and human activity recognition (HAR). In order to improve the accuracy of the HAR system, a novel deep learning ensemble (DL-Ens) model composed of three lightweight convolutional and recurrent neural networks is presented in this study. Evaluation of the activity recognition performance of the suggested DL-Ens approach is carried out on a self-recorded dataset acquired using multiple wearable motion sensors as well as on the publicly accessible UCI's human activity recognition (UCI-HAR) dataset. The individual deep learning models are tested for time-series classification. However, the proposed DL-Ens approach achieves the highest classification accuracy of 97.48±5.02% on the self-recorded dataset and 93.36±5.89% on the UCI-HAR dataset.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".