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Applied Sciences
Special Issues
Novel Approaches for Human Activity Recognition
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Special Issue "Novel Approaches for Human Activity Recognition"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2023 | Viewed by 1191

Special Issue Editors

Prof. Dr. Marc Kurz

E-Mail Website
Guest Editor
Department of Mobility and Energy, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria
Interests: mobile software systems; frameworks and architectures; activity and context recognition; Internet of Things; distributed and autonomic computing; adaptive and self-adaptive systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Erik Sonnleitner

E-Mail Website
Guest Editor
Department of Mobility & Energy, University of Applied Sciences Upper Austria, 4232 Hagenberg, Austria
Interests: system security; mobile device security; blockchains and distributed ledger technology; web security; authentication and authorization; information hiding
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Clemens Holzmann
E-Mail Website
Guest Editor
Department for Smart and Interconnected Living, University of Applied Sciences Upper Austria, 4232 Hagenberg im Muehlkreis, Austria
Interests: human-computer interaction; interactive technologies; mobile interfaces; mobile computing

Special Issue Information

Dear Colleagues,

With the advent of mobile systems in recent decades, people are ever increasingly connected to smart devices. These devices aim to make our lives more comfortable and assist in different situations—the most prominent examples of such devices might be the mobile phone, or wearable and ubiquitous systems in general. Additionally, these devices have become more powerful and are able to compute complex calculations.

This combination of powerful computational devices and permanent connectivity opens new chances for approaches in the area of human activity recognition. With the constant improvement of algorithmic methods and the definition of new technologies (deep learning, etc.) human activity recognition as it has been done for decades faces novel and exciting approaches. Since human activity recognition heavily deals with personal data, security and privacy aspects are also of high relevance for this Special Issue.

Potential topics of interest for this Special Issue include (but are not limited to) the following:

  • Human activity recognition;
  • Machine learning;
  • Deep learning;
  • Neural networks;
  • Wearable and mobile systems;
  • Security and privacy;
  • Ambient Intelligence;
  • Artificial intelligence;
  • Ambient intelligence;
  • Ubiquitous computing;
  • Pervasive and embedded systems;
  • Security aspects for mobile systems;
  • Sensing with smartphones and wearables.

Prof. Dr. Marc Kurz
Prof. Dr. Erik Sonnleitner
Prof. Dr. Clemens Holzmann
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. Applied Sciences 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 2300 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.

Published Papers (2 papers)

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Article
Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
by
Xiaocheng Huang
,
Youwei Yuan
,
Chaoqi Chang
,
Yiming Gao
,
Chao Zheng
and
Lamei Yan
Appl. Sci. 2023, 13(16), 9059; https://doi.org/10.3390/app13169059 - 08 Aug 2023
Viewed by 118
Abstract
Human Activity Recognition (HAR) has been proven to be effective in various healthcare and telemonitoring applications. Current HAR methods, especially deep learning, are extensively employed owing to their exceptional recognition capabilities. However, in pursuit of enhancing feature expression abilities, deep learning often introduces [...] Read more.
Human Activity Recognition (HAR) has been proven to be effective in various healthcare and telemonitoring applications. Current HAR methods, especially deep learning, are extensively employed owing to their exceptional recognition capabilities. However, in pursuit of enhancing feature expression abilities, deep learning often introduces a trade-off by increasing Time complexity. Moreover, the intricate nature of human activity data poses a challenge as it can lead to a notable decrease in recognition accuracy when affected by additional noise. These aspects will significantly impair recognition performance. To advance this field further, we present a HAR method based on an edge-computing-assisted and GRU deep-learning network. We initially proposed a model for edge computing to optimize the energy consumption and processing time of wearable devices. This model transmits HAR data to edge-computable nodes, deploys analytical models on edge servers for remote training, and returns results to wearable devices for processing. Then, we introduced an initial convolution method to preprocess large amounts of training data more effectively. To this end, an attention mechanism was integrated into the network structure to enhance the analysis of confusing data and improve the accuracy of action classification. Our results demonstrated that the proposed approach achieved an average accuracy of 85.4% on the 200 difficult-to-identify HAR data, which outperforms the Recurrent Neural Network (RNN) method’s accuracy of 77.1%. The experimental results showcase the efficacy of the proposed method and offer valuable insights for the future application of HAR. Full article
(This article belongs to the Special Issue Novel Approaches for Human Activity Recognition)
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Article
Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
by
Seksan Laitrakun
Appl. Sci. 2023, 13(4), 2475; https://doi.org/10.3390/app13042475 - 14 Feb 2023
Viewed by 685
Abstract
Human activity recognition (HAR) has been applied to several advanced applications, especially when individuals may need to be monitored closely. This work focuses on HAR using wearable sensors attached to various locations of the user body. The data from each sensor may provide [...] Read more.
Human activity recognition (HAR) has been applied to several advanced applications, especially when individuals may need to be monitored closely. This work focuses on HAR using wearable sensors attached to various locations of the user body. The data from each sensor may provide unequally discriminative information and, then, an effective fusion method is needed. In order to address this issue, inspired by the squeeze-and-excitation (SE) mechanism, we propose the merging-squeeze-excitation (MSE) feature fusion which emphasizes informative feature maps and suppresses ambiguous feature maps during fusion. The MSE feature fusion consists of three steps: pre-merging, squeeze-and-excitation, and post-merging. Unlike the SE mechanism, the set of feature maps from each branch will be recalibrated by using the channel weights also computed from the pre-merged feature maps. The calibrated feature maps from all branches are merged to obtain a set of channel-weighted and merged feature maps which will be used in the classification process. Additionally, a set of MSE feature fusion extensions is presented. In these proposed methods, three deep-learning models (LeNet5, AlexNet, and VGG16) are used as feature extractors and four merging methods (addition, maximum, minimum, and average) are applied as merging operations. The performances of the proposed methods are evaluated by classifying popular public datasets. Full article
(This article belongs to the Special Issue Novel Approaches for Human Activity Recognition)
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