Deep Incremental Learning for Personalized Human Activity Recognition on Edge Devices
Bibliographic record
Abstract
Tracking human daily activities is a useful functionality supported in many applications, especially with the pervasive use of wearable devices. State-of-the-art human activity recognition (HAR) uses machine or deep learning techniques to identify activities based on sensor readings. However, these models represent patterns from standardized experiment setups, with limited diversity when it comes to the individuals involved in data collection. This leads to limited success of HAR in real deployment scenarios, where individuals perform the same activity in different ways. Training models from scratch on real-time data streams is challenging due to the computational complexity of machine and deep learning architectures. In this article, we propose an incremental learning model for HAR that tweaks a deep learning model pretrained on a standardized HAR dataset and incrementally trains on newly generated individuals personalized data on their personal devices. The proposed solution promotes the preservation of data privacy, improves the model performance in terms of accuracy and efficiency without having to retrain from scratch, and tweaks the model according to personalized activity patterns. Extensive experiments show improvement of the base model’s accuracy up to 19% after incrementally training the model on filtered users’ datasets for the standing, walking, and running activities.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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 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".