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Electronics
Special Issues
Techniques and Advances in Human Activity Recognition
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Techniques and Advances in Human Activity Recognition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Flexible Electronics".

Deadline for manuscript submissions: closed (16 June 2024) | Viewed by 3394

Special Issue Editors

Dr. Ana Kuzmanic Skelin

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Ruđera Boškovića 35, 21000 Split, Croatia
Interests: electronics; human activity recognition; elderly physical activity recognition; machine learning; body-worn sensors; wearables; biosensors; augmented reality; computer vision
Dr. Maya Dimitrova

E-Mail Website
Guest Editor
The Institute of Robotics, Bulgarian Academy of Science, PO Box 79, 1113 Sofia, Bulgaria
Interests: human-robot interaction; brain-like intelligent agents; pedagogical rehabilitation; socially competent robotic systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mirjana Bonkovic

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Ruđera Boškovića 35, 21000 Split, Croatia
Interests: robotics; vision based control; computer vision; signal processing

Special Issue Information

Dear Colleagues,

Human activity recognition (HAR) is a research area and potentially an enabling technology important in various application domains such as robotics, surveillance, assistance, sports, and healthcare. 

HAR is constantly evolving in terms of efficiency and accuracy due to the advancement in sensing devices, the development of data processing techniques and ubiquitous computing. At the same time, this increased technical and computational complexity opens up a scope for improvements in the various supporting sensory approaches and algorithmic solutions that will bring us closer to using HAR in real-world environments.

The objective of this Special Issue to seek the submissions from academia and industry presenting original research with theoretical and practical contributions to human activity recognition. Topics of interest include, but are not limited to, the following:

  • Multi-sensor information fusion;
  • Sensor modalities integration;
  • Wearable devices;
  • Interactions and crowd activity modelling;
  • Har in real-world scenarios: complex and ambiguous activities/out-of-lab environments;
  • Dataset labelling strategies;
  • Deep learning and machine learning;
  • Feature engineering;
  • Contextual information modelling;
  • Performance metric in HAR;
  • IoT platforms for HAR.

Dr. Ana Kuzmanic Skelin
Dr. Maya Dimitrova
Prof. Dr. Mirjana Bonkovic
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wearable devices
  • human activity recognition
  • Deep Learning
  • Machine Learning
  • feature engineering

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Published Papers (2 papers)

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Research

26 pages, 7644 KiB  
Article
Damage Detection in Structural Health Monitoring Using an Integrated ANNIRSA Approach
by Ngoc Dung Bui, Minh Dang and Tran Hieu Nguyen
Electronics 2024, 13(7), 1241; https://doi.org/10.3390/electronics13071241 - 27 Mar 2024
Viewed by 840
Abstract
In the past decade, artificial neural networks (ANNs) have been widely employed to address many problems. Despite their powerful problem-solving capabilities, ANNs are susceptible to a significant risk of stagnation in local minima due to using backpropagation algorithms based on gradient descent (GD) [...] Read more.
In the past decade, artificial neural networks (ANNs) have been widely employed to address many problems. Despite their powerful problem-solving capabilities, ANNs are susceptible to a significant risk of stagnation in local minima due to using backpropagation algorithms based on gradient descent (GD) for optimal solution searching. In this paper, we introduce an enhanced version of the reptile search algorithm (IRSA), which operates in conjunction with an ANN to mitigate these limitations. By substituting GD with IRSA within an ANN, the network gains the ability to escape local minima, leading to improved prediction outcomes. To demonstrate the efficacy of IRSA in enhancing ANN’s performance, a numerical model of the Nam O Bridge is utilized. This model is updated to closely reflect actual structural conditions. Consequently, damage scenarios for single-element and multielement damage within the bridge structure are developed. The results confirm that ANNIRSA offers greater accuracy than traditional ANNs and ANNRSAs in predicting structural damage. Full article
(This article belongs to the Special Issue Techniques and Advances in Human Activity Recognition)
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24 pages, 1571 KiB  
Article
To Transfer or Not to Transfer and Why? Meta-Transfer Learning for Explainable and Controllable Cross-Individual Activity Recognition
by Qiang Shen, Stefano Teso, Fausto Giunchiglia and Hao Xu
Electronics 2023, 12(10), 2275; https://doi.org/10.3390/electronics12102275 - 18 May 2023
Cited by 2 | Viewed by 2153
Abstract
Human activity recognition (HAR) plays a central role in ubiquitous computing applications such as health monitoring. In the real world, it is impractical to perform reliably and consistently over time across a population of individuals due to the cross-individual variation in human behavior. [...] Read more.
Human activity recognition (HAR) plays a central role in ubiquitous computing applications such as health monitoring. In the real world, it is impractical to perform reliably and consistently over time across a population of individuals due to the cross-individual variation in human behavior. Existing transfer learning algorithms suffer the challenge of “negative transfer”. Moreover, these strategies are entirely black-box. To tackle these issues, we propose X-WRAP (eXplain, Weight and Rank Activity Prediction), a simple but effective approach for cross-individual HAR, which improves the performance, transparency, and ease of control for stakeholders in HAR. X-WRAP works by wrapping transfer learning into a meta-learning loop that identifies the approximately optimal source individuals. The candidate source domains are ranked using a linear scoring function based on interpretable meta-features capturing the properties of the source domains. X-WRAP is optimized using Bayesian optimization. Experiments conducted on a publicly available dataset show that the model can effectively improve the performance of transfer learning models consistently. In addition, X-WRAP can provide interpretable analysis according to the meta-features, making it possible for stakeholders to get a high-level understanding of selective transfer. In addition, an extensive empirical analysis demonstrates the promise of the approach to outperform in data-sparse situations. Full article
(This article belongs to the Special Issue Techniques and Advances in Human Activity Recognition)
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