Continuous Human Activity Recognition in IoT Environments With BAOA and AFSG-TPD GANs
Bibliographic record
Abstract
We propose a method for recognizing continuous indoor daily human activities among continuous indoor Internet of Things (IoT) smart environments using millimeter wave radar. Focusing on the following problems: 1) transition errors due to random transitions between actions during continuous action recognition and 2) the time-consuming and labor-intensive factors on radar data acquisition for continuous human actions make it difficult to consider all action sequences, and the network suffers from catastrophic degradation of the recognition performance when faced with completely new human action sequences. A bounding adaptive optimization algorithm based on interlacing error (BAOA) and a generative adversarial network based on adaptive feature selection generator and temporal patch discriminator (AFSG-TPD GAN) are proposed. BAOA is used to accurately segment the existing action sequences to obtain a single action dataset of random duration, synthesize the data used to train the AFSG-TPD GAN, generate new action sequences, and train the recognition network to improve generalization performance. After the comparison test, BAOA increases the average accuracy by 3.91% compared to the state-of-the-art (SOTA) method. Meanwhile, the network trained with the data generated by the AFSG-TPD GAN overcomes the problem of catastrophic degradation of the recognition performance when confronted with brand new human action sequences in real-world tests, and the average accuracy is improved from 65.01% to 94.85%.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".