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Electronics
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State-of-the-Art of Human Activity Recognition
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State-of-the-Art of Human Activity Recognition

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 5169

Special Issue Editors

Dr. Juan José Pantrigo

E-Mail Website1 Website2
Guest Editor
Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
Interests: artificial intelligence; machine learning; deep learning; reinforcement learning; computer vision; exact and heuristic optimization methods; object detection; multiple object tracking; video surveillance; articulated motion tracking; vision-based human activity recognition; computer vision in transportation systems
Dr. Raúl Cabido

E-Mail Website
Guest Editor
Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
Interests: computer vision; machine learning; deep learning; GPU computing; articulated human motion tracking; pose estimation; video surveillance; vision-based human activity recognition; 3D reconstruction

Special Issue Information

Dear Colleagues,

Human activity recognition is a research topic with increasing interest among the scientific community. The number of papers published in this subject has increased significantly in the last few years, and with the rise of new techniques, these systems can now deal with real-world conditions such as moving cameras, changing illumination conditions, etc. The potential applications of human activity recognition systems involve fields as sports, gaming, surveillance, smart environments, human­–machine interaction, image and video retrieval, and automatic video editing, to cite only a few. This Special Issue is intended to present a collection of representative scientific papers that give an idea of the current state-of-the-art of human activity recognition, focusing on methods, applications, and datasets.

Dr. Juan José Pantrigo
Dr. Raúl Cabido
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

  • Human action/activity/gesture recognition
  • Large datasets for action/activity/gesture recognition
  • Human action recognition using deep learning
  • Multisensor action recognition
  • Action recognition using depth maps or skeleton data
  • Recognition of human interactions
  • Human–object interaction recognition
  • Behavior recognition
  • Action recognition in the crowd
  • Early action prediction
  • Human action generation
  • Anomaly detection in surveillance videos

Published Papers (2 papers)

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Research

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20 pages, 2209 KiB  
Article
Active Sense: Early Staging of Non-Insulin Dependent Diabetes Mellitus (NIDDM) Hinges upon Recognizing Daily Activity Pattern
by Erfanul Hoque Bahadur, Abdul Kadar Muhammad Masum, Arnab Barua and Md Zia Uddin
Electronics 2021, 10(18), 2194; https://doi.org/10.3390/electronics10182194 - 08 Sep 2021
Cited by 3 | Viewed by 1848
Abstract
The Human Activity Recognition (HAR) system allows various accessible entries for the early diagnosis of Diabetes as one of the nescient applications domains for the HAR. Long Short-Term Memory (LSTM) was applied and recognized 13 activities that resemble diabetes symptoms. Afterward, risk factor [...] Read more.
The Human Activity Recognition (HAR) system allows various accessible entries for the early diagnosis of Diabetes as one of the nescient applications domains for the HAR. Long Short-Term Memory (LSTM) was applied and recognized 13 activities that resemble diabetes symptoms. Afterward, risk factor assessment for an experimental subject identified similar activity pattern attributes between diabetic patients and the experimental subject. Because of this, a trained LSTM model was deployed to monitor the average time length for every activity performed by the experimental subject for 30 consecutive days. Concurrently, the symptomatic diabetes activity patterns of diabetic patients were explored. The cosine similarity of activity patterns of the experimental subject and diabetic patients measured 57.39%, putting the experimental subject into moderate risk factor class. The experimental subject was clinically tested for risk factors using the diabetic clinical diagnosis process, known as the A1C. The A1C level was 6.1%, recognizing the experimental subject as a patient suffering from Diabetes. Thus, the proposed novel approach remarkably classifies the risk factor level based on activity patterns. Full article
(This article belongs to the Special Issue State-of-the-Art of Human Activity Recognition)
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Review

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23 pages, 6060 KiB  
Review
A Survey of Vision-Based Transfer Learning in Human Activity Recognition
by David Ada Adama, Ahmad Lotfi and Robert Ranson
Electronics 2021, 10(19), 2412; https://doi.org/10.3390/electronics10192412 - 02 Oct 2021
Cited by 6 | Viewed by 2538
Abstract
Human activity recognition (HAR) and transfer learning (TL) are two broad areas widely studied in computational intelligence (CI) and artificial intelligence (AI) applications. Much effort has been put into developing suitable solutions to advance the current performance of existing systems. However, challenges are [...] Read more.
Human activity recognition (HAR) and transfer learning (TL) are two broad areas widely studied in computational intelligence (CI) and artificial intelligence (AI) applications. Much effort has been put into developing suitable solutions to advance the current performance of existing systems. However, challenges are facing the existing methods of HAR. In HAR, the variations in data required in HAR systems pose challenges to many existing solutions. The type of sensory information used could play an important role in overcoming some of these challenges. Vision-based information in 3D acquired using RGB-D cameras is one type. Furthermore, with the successes encountered in TL, HAR stands to benefit from TL to address challenges to existing methods. Therefore, it is important to review the current state-of-the-art related to both areas. This paper presents a comprehensive survey of vision-based HAR using different methods with a focus on the incorporation of TL in HAR methods. It also discusses the limitations, challenges and possible future directions for more research. Full article
(This article belongs to the Special Issue State-of-the-Art of Human Activity Recognition)
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