Deep Convolutional Neural Network Learning for Activity Recognition using real-life sensor’s data in smart devices
Bibliographic record
Abstract
Human activity recognition (HAR) recently has garnered a lot of attention in recent years especially in wearable smart devices due to its high demand in various application domains. Smart-devices are nowadays a perfect way of collecting personalized data from users. The data collecting from sensors in smart devices can be used for many purposes like health care, lifelogging or fitness. In this research, we propose an approach to have efficient and effective activity recognition with real-life data coming from smart devices. The proposed approach is based on Deep Convolutional Neural Network learning and is combined with an active learning approach which adds personalized recognition based on personalized data to the model. Experiments show the proposed model has the perfect efficiency with the state-of-the-art approach and with the personalized data and the personalized recognition for walking while holding a phone in different positions, and for all positions of holding phone and laying. Beside locomotion activities, more actions like sweeping, vacuuming have been analyzed with promising results.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".