Enhancing Human Action Recognition with Asymmetric Generalized Gaussian Mixture Model-Based Hidden Markov Models and Bounded Support
Bibliographic record
Abstract
Human action recognition (HAR) is a crucial research field that necessitates the implementation of advanced mathematical concepts to recognize human activities from sequences of observations. This paper presents a novel framework employing a mixture-based Hidden Markov Model (HMM) that capitalizes on the advantages of asymmetric modeling, bounded support, and robustness in sensor-based HAR. To accommodate variations in observations within each human activity class, we propose an asymmetric generalized Gaussian mixture model (AGGM) to model the emission probabilities. Subsequently, we propose incorporating the bounded asymmetric generalized Gaussian mixture model (BAGGM) to address the constraints inherent in real-life data. The parameters of the corresponding HMM are estimated using the Baum-Welch algorithm, and the most probable sequence of hidden states is inferred using the Viterbi algorithm. We validate our proposed framework using four datasets for human activities. Experimental results demonstrate that our proposed model outperforms all state-of-the-art HAR models that are HMM-based, thereby emphasizing the superiority of our proposed frameworks in HAR systems to understand behaviours, predict potential actions, and facilitate sports applications for the general population and independent living applications for vulnerable populations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".