Noise-Resilient Human Activity Recognition via A Hybrid CNN–InceptionV3 Model
Bibliographic record
Abstract
Human activity recognition (HAR) is the process of identifying and classifying physical activities performed by individuals through data captured from various sensors.It is essential for applications like sports analysis, rehabilitation, patient monitoring, and senior care systems.From the literature it is observed that human activity recognition (HAR) models developed in an unconstrained environment have several limitations like Personal Interference (PI), Electromagnetic (EM) noise, in-band noise or human movements and outliers involved while capturing the input data.These noises are affecting the overall performance and robustness of the model.In order to improve the model performance, noise removal techniques are introduced in this work.Noises like salt and pepper noise, Gaussian noise, and blurring of the boundaries and outlier treatment are processed for the hybrid data acquired using video and sensors in line of sight mode in this paper.For removing these noises, a combination of filters like Kalman filter, MOSSE filter, Butter-worth and J filter, are applied to the input data.By developing this noise removal technique, Peak to Sidelobe Ratio (PSR) is reduced from the raw data.After removing these noises, features are extracted using the top layers of CNN InceptionV3 model for video data.Similarly by using inertial sensor features like tri-axial accelerometer, gyroscopes and magnetometers are collected and a feature vector is created.Pyramidal flow feature fusion (PFFF) technique is used to fuse the extracted features.Finally, the fused features are given to the Support Vector Machine (SVM) classifier to perform activity recognition.This work presents a hybrid deep learning model designed to enhance noise resilience in human activity recognition (HAR) for unconstrained environments and it was validated on UCF Sports and UCI HAR datasets, showing that noise removal with hybrid feature fusion markedly improves HAR model robustness.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".