Trends in human activity recognition with focus on machine learning and power requirements
Bibliographic record
Abstract
The advancement and availability of technology can be employed to improve our daily lives. One example is Human Activity Recognition (HAR). HAR research has been mainly explored using imagery but is currently evolving to the use of sensors and has the ability to have a positive impact, including individual health monitoring and removing the barrier of healthcare. To reach a marketable HAR device, state-of-the-art classifications and power consumption methods such as convolutional neural network (CNN), data compression and other emerging techniques are reviewed here. The review of the current literature creates a foundation in HAR and addresses the lack of available HAR datasets, recommendation of classification and power reduction techniques, current drawbacks and their respective solutions, as well as future trends in HAR. The lack of publicly available datasets makes it difficult for new users to explore the field of HAR. This paper dedicates a section to publicly available datasets for users to access. Finally, a framework is suggested for HAR applications, which envelopes the current literature and emerging trends in HAR.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".