Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 16, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K

Human Activity Recognition with Noise-Injected Time-Distributed AlexNet.

Sanjay Dutta1, Tossapon Boongoen1, Reyer Zwiggelaar1

  • 1Department of Computer Science, Aberystwyth University, Ceredigion SY23 3DB, UK.

Biomimetics (Basel, Switzerland)
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Weakly supervised pathological differentiation of primary central nervous system lymphoma and glioblastoma on multi-site whole slide images.

Journal of medical imaging (Bellingham, Wash.)·2025
Same author

A systematic review of the prediction of hospital length of stay: Towards a unified framework.

PLOS digital health·2023
Same author

Single-Input Multi-Output U-Net for Automated 2D Foetal Brain Segmentation of MR Images.

Journal of imaging·2021
Same author

Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring.

Journal of imaging·2021
Same author

Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier.

Journal of imaging·2021
Same author

Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation.

Journal of imaging·2021
Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026
Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026
Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026
Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026
See all related articles

This study enhances human activity recognition (HAR) by integrating biologically inspired noise injection into a time-distributed AlexNet model. This approach improves system accuracy and robustness for real-world applications.

Area of Science:

  • Computer Vision
  • Deep Learning
  • Biologically Inspired Computing

Background:

  • Human Activity Recognition (HAR) is crucial for applications in healthcare, security, and smart environments.
  • Traditional AlexNet architectures are designed for static images and require adaptation for temporal video data.
  • Overfitting and poor generalization limit the performance of time-distributed AlexNet models in HAR.

Purpose of the Study:

  • To enhance the performance and robustness of HAR systems.
  • To integrate biologically inspired noise injection with a time-distributed AlexNet architecture.
  • To evaluate the impact of noise injection on model accuracy, stability, and overall performance.

Main Methods:

  • Adapted AlexNet architecture using a time-distributed approach for video classification.
Keywords:
AlexNetdeep learninghuman activity recognitionnoise injectionregularisation techniques

Related Experiment Videos

Last Updated: Jan 16, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
  • Incorporated Gaussian noise injection at the input level during training, inspired by biological sensory processing.
  • Conducted experiments on EduNet, UCF50, and UCF101 datasets to assess model performance.

    • Main Results:

    • The bio-inspired noise-injected time-distributed AlexNet achieved 91.40% accuracy and 92.77% F1 score.
    • The proposed model outperformed existing state-of-the-art models on the tested datasets.
    • Hyperparameter tuning, especially learning rate optimization, improved model stability and reduced result variance.

    Conclusions:

    • The strategic combination of noise injection and time-distributed architectures significantly improves HAR generalisation and robustness.
    • This approach offers a pathway towards resource-efficient and deployable deep learning systems for real-world HAR.
    • Biologically inspired noise injection is an effective technique for enhancing deep learning model performance in dynamic environments.