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Novel Approaches for Human Activity Recognition

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 4419

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Special Issue Editors

Prof. Dr. Marc Kurz

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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
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Prof. Dr. Erik Sonnleitner

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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

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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

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Research

24 pages, 9475 KiB

Open AccessArticle

A Hybrid Human Activity Recognition Method Using an MLP Neural Network and Euler Angle Extraction Based on IMU Sensors

by Yaxin Mao, Lamei Yan, Hongyu Guo, Yujie Hong, Xiaocheng Huang and Youwei Yuan

Appl. Sci. 2023, 13(18), 10529; https://doi.org/10.3390/app131810529 - 21 Sep 2023

Cited by 1 | Viewed by 862

Abstract

Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. It has propelled the extensive application of HAR across various domains. In the healthcare sector, HAR finds utility in monitoring and assessing movements during rehabilitation processes, while in the sports science field, it contributes to enhancing training outcomes and preventing exercise-related injuries. However, traditional sensor fusion algorithms often require intricate mathematical and statistical processing, resulting in higher algorithmic complexity. Additionally, in dynamic environments, sensor states may undergo changes, posing challenges for real-time adjustments within conventional fusion algorithms to cater to the requirements of prolonged observations. To address these limitations, we propose a novel hybrid human pose recognition method based on IMU sensors. The proposed method initially calculates Euler angles and subsequently refines them using magnetometer and gyroscope data to obtain the accurate attitude angle. Furthermore, the application of FFT (Fast Fourier Transform) feature extraction facilitates the transition of the signal from its time-based representation to its frequency-based representation, enhancing the practical significance of the data. To optimize feature fusion and information exchange, a group attention module is introduced, leveraging the capabilities of a Multi-Layer Perceptron which is called the Feature Fusion Enrichment Multi-Layer Perceptron (GAM-MLP) to effectively combine features and generate precise classification results. Experimental results demonstrated the superior performance of the proposed method, achieving an impressive accuracy rate of 96.13% across 19 different human pose recognition tasks. The proposed hybrid human pose recognition method is capable of meeting the demands of real-world motion monitoring and health assessment. Full article

(This article belongs to the Special Issue Novel Approaches for Human Activity Recognition)

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16 pages, 1679 KiB

Open AccessArticle

Let Me Help You: Improving the Novice Experience of High-Performance Keyboard Layouts with Visual Clues

by Dominik Grüneis, Marc Kurz and Erik Sonnleitner

Appl. Sci. 2023, 13(16), 9391; https://doi.org/10.3390/app13169391 - 18 Aug 2023

Viewed by 699

Abstract

Since the advent of smartphones, work tasks shift progressively towards mobile phones. The main hurdle of this progression is the substantially slower input speed of smartphones in comparison with physical keyboards. This input speed could be improved by using keyboard layouts optimized for use with smartphones. As these keyboard layouts are not commonly used, switching to them results in a poor novice experience with an initially decreased text input speed. Previous investigations confirmed that this poor novice experience can be attenuated by using visual clues accentuating the most probable keys. This gives rise to the question of which visual impression leads to the best performance improvement. Therefore, this article evaluates the performance of six visual clues with different visual impressions via a user study conducted with 28 participants. The results showed that the visual clue with a pop-up animation and the visual clue with an increased font size performed the best with a 4% and 3%, respectively, typing performance improvement. Nonetheless, the questionnaire and personal preferences part of the user study showed that a 4% performance gain is not enough to crown one individual visual clue as the best visual clue to be used with every individual. This leads to the conclusion that functionality to choose one personal preference is more important than focusing on one specific best clue. Full article

(This article belongs to the Special Issue Novel Approaches for Human Activity Recognition)

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18 pages, 5189 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1195

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 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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24 pages, 1620 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1131

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 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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