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Innovations in Wireless Sensor-Based Human Activity Recognition
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Innovations in Wireless Sensor-Based Human Activity Recognition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (25 June 2024) | Viewed by 9814

Special Issue Editors

Dr. Qingquan Sun

E-Mail Website
Guest Editor
School of Computer Science and Engineering, California State University San Bernardino, San Bernardino, CA 92407, USA
Interests: intelligent sensing; machine learning; Internet of Things; cyber-physical systems
Dr. Jiang Lu

E-Mail Website
Guest Editor
College of Science and Engineering, University of Houston-Clear Lake, 2700 Bay Area Blvd, Houston, TX 77058, USA
Interests: wireless communication; intelligent sensor systems; wireless healthcare
Dr. Xin Liu

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
Interests: cognitive radio; internet of things; spectrum sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xinlin Huang

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Tongji University, Shanghai 201804, China
Interests: cognitive radio, multimedia transmission, and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human activity recognition (HAR) technology aims to identify the activities and interactions of human subjects through their motions, actions, and behavioural patterns. Wireless-sensor-based human activity recognition has become a vibrant research domain and attracted significant attention due to its broad applications, which range from animation, gaming, and sports to well-being, healthcare, assisted living, monitoring, home automation, energy-efficient buildings, and smart environments. Although wireless-sensor-based human activity recognition has been widely investigated with multiple modalities of sensors and a variety of modern sensing technologies, the development of  accurate and robust human activity recognition systems still faces many challenges, including overcoming the low accuracy/sensitivity of sensors, low-information data, the lack of continual learning capability, the complexity of multi-module, multi-modality data fusion, hierarchy of activities, and privacy issues.

Recent breakthroughs in materials, architectures, and fabrications in wireless sensors as well as in wireless sensing technologies have enabled researchers to develop more accurate and robust human activity recognition systems and corresponding emerging applications. These systems may be used for both individual activities and group activities.

This Special Issue will focus on recent innovations in wireless sensors, sensing systems, and sensing technologies advancing human activity recognition technology. Both original research papers and reviews are welcome.

Dr. Qingquan Sun
Dr. Jiang Lu
Dr. Xin Liu
Prof. Dr. Xinlin Huang
Guest Editors

Manuscript Submission Information

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Keywords

  • smart sensors
  • innovations in sensing systems
  • innovations in multimodality sensor data fusion
  • innovations in hybrid modality sensor data fusion
  • emerging sensing technologies
  • novel machine learning and deep learning models
  • human–machine interactions
  • low-power energy-efficient sensor systems

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Published Papers (4 papers)

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Research

18 pages, 2279 KiB  
Article
A Novel Lightweight Human Activity Recognition Method Via L-CTCN
by Xue Ding, Zhiwei Li, Jinyang Yu, Weiliang Xie, Xiao Li and Ting Jiang
Sensors 2023, 23(24), 9681; https://doi.org/10.3390/s23249681 - 7 Dec 2023
Cited by 2 | Viewed by 1377
Abstract
Wi-Fi-based human activity recognition has attracted significant attention. Deep learning methods are widely used to achieve feature representation and activity sensing. While more learnable parameters in the neural networks model lead to richer feature extraction, it results in significant resource consumption, rendering the [...] Read more.
Wi-Fi-based human activity recognition has attracted significant attention. Deep learning methods are widely used to achieve feature representation and activity sensing. While more learnable parameters in the neural networks model lead to richer feature extraction, it results in significant resource consumption, rendering the model unsuitable for lightweight Internet of Things (IoT) devices. Furthermore, the sensing performance heavily relies on the quality and quantity of data, which is a time-consuming and labor-intensive task. Therefore, there is a need to explore methods that reduce the dependence on the quality and quantity of the dataset while ensuring recognition performance and decreasing model complexity to adapt to ubiquitous lightweight IoT devices. In this paper, we propose a novel Lightweight-Complex Temporal Convolution Network (L-CTCN) for human activity recognition. Specifically, this approach effectively combines complex convolution with a Temporal Convolution Network (TCN). Complex convolution can extract richer information from limited raw complex data, reducing the reliance on the quality and quantity of training samples. Based on the designed TCN framework with 1D convolution and residual blocks, the proposed model can achieve lightweight human activity recognition. Extensive experiments verify the effectiveness of the proposed method. We can achieve an average recognition accuracy of 96.6% with only 0.17 M parameter size. This method performs well under conditions of low sampling rates and a low number of subcarriers and samples. Full article
(This article belongs to the Special Issue Innovations in Wireless Sensor-Based Human Activity Recognition)
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18 pages, 794 KiB  
Article
Human Activity Recognition via Score Level Fusion of Wi-Fi CSI Signals
by Gunsik Lim, Beomseok Oh, Donghyun Kim and Kar-Ann Toh
Sensors 2023, 23(16), 7292; https://doi.org/10.3390/s23167292 - 21 Aug 2023
Cited by 3 | Viewed by 2813
Abstract
Wi-Fi signals are ubiquitous and provide a convenient, covert, and non-invasive means of recognizing human activity, which is particularly useful for healthcare monitoring. In this study, we investigate a score-level fusion structure for human activity recognition using the Wi-Fi channel state information (CSI) [...] Read more.
Wi-Fi signals are ubiquitous and provide a convenient, covert, and non-invasive means of recognizing human activity, which is particularly useful for healthcare monitoring. In this study, we investigate a score-level fusion structure for human activity recognition using the Wi-Fi channel state information (CSI) signals. The raw CSI signals undergo an important preprocessing stage before being classified using conventional classifiers at the first level. The output scores of two conventional classifiers are then fused via an analytic network that does not require iterative search for learning. Our experimental results show that the fusion provides good generalization and a shorter learning processing time compared with state-of-the-art networks. Full article
(This article belongs to the Special Issue Innovations in Wireless Sensor-Based Human Activity Recognition)
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19 pages, 2517 KiB  
Article
Cross-Domain Sentiment Analysis Based on Feature Projection and Multi-Source Attention in IoT
by Yeqiu Kong, Zhongwei Xu and Meng Mei
Sensors 2023, 23(16), 7282; https://doi.org/10.3390/s23167282 - 20 Aug 2023
Cited by 8 | Viewed by 2503
Abstract
Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to [...] Read more.
Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models. Full article
(This article belongs to the Special Issue Innovations in Wireless Sensor-Based Human Activity Recognition)
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16 pages, 4222 KiB  
Article
Research on Lightweight Microservice Composition Technology in Cloud-Edge Device Scenarios
by Hanqi Li, Xianhui Liu and Weidong Zhao
Sensors 2023, 23(13), 5939; https://doi.org/10.3390/s23135939 - 26 Jun 2023
Viewed by 1882
Abstract
In recent years, cloud-native technology has become popular among Internet companies. Microservice architecture solves the complexity problem for multiple service methods by decomposing a single application so that each service can be independently developed, independently deployed, and independently expanded. At the same time, [...] Read more.
In recent years, cloud-native technology has become popular among Internet companies. Microservice architecture solves the complexity problem for multiple service methods by decomposing a single application so that each service can be independently developed, independently deployed, and independently expanded. At the same time, domestic industrial Internet construction is still in its infancy, and small and medium-sized enterprises still face many problems in the process of digital transformation, such as difficult resource integration, complex control equipment workflow, slow development and deployment process, and shortage of operation and maintenance personnel. The existing traditional workflow architecture is mainly aimed at the cloud scenario, which consumes a lot of resources and cannot be used in resource-limited scenarios at the edge. Moreover, traditional workflow is not efficient enough to transfer data and often needs to rely on various storage mechanisms. In this article, a lightweight and efficient workflow architecture is proposed to optimize the defects of these traditional workflows by combining cloud-edge scene. By orchestrating a lightweight workflow engine with a Kubernetes Operator, the architecture can significantly reduce workflow execution time and unify data flow between cloud microservices and edge devices. Full article
(This article belongs to the Special Issue Innovations in Wireless Sensor-Based Human Activity Recognition)
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