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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".