A Novel Activity Pattern Recognition via Convolutional Neural Networks and Advanced Skeleton Models
Bibliographic record
Abstract
Human Activity Recognition (HAR) is crucial to intelligent smart home systems.In this research, we propose a novel skeleton-based method for recognizing human activities accurately.Gamma correction is applied as a preprocessing step to improve image quality.Then, we use a robust combination of Multiple Object Tracking (MOT) and graph-based segmentation techniques to extract precise human silhouettes from video sequences.This research also introduces a novel innovation in developing a 23-joint skeleton model that accurately identifies and tracks key body joints.A comprehensive set of features extracted from this skeleton data is derived, including relative joint angles, joint proximity measures, joint stability, and full body features, which are extracted using BRIEF, LATCH, and MSER.A fuzzy optimization technique is employed to find the most discriminative features to optimize feature selection.Finally, a Convolutional Neural Networks (CNN) classifier is trained on the optimized features to classify human activities accurately.Experimental results demonstrate the effectiveness of our approach, with ShakeFive2 achieving an 88% accuracy rate and BIT-Interaction achieving 94% on a benchmark dataset.This work contributes to advancing human activity understanding in various domains, such as surveillance, human-behavior interaction, healthcare, sports, and social robotics.
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 | 0.000 | 0.000 |
| 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".