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Computers
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Wearable Computing and Activity Recognition
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Wearable Computing and Activity Recognition

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 5306

Special Issue Editors

Prof. Dr. Yang Gao

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Guest Editor
Computer Science, East China Normal University, Shanghai 200241, China
Interests: pervasive computing; human–computer-interaction; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Yincheng Jin

E-Mail Website
Guest Editor
Computer Science, Binghamton University—State University of New York, Binghamton, NY 13902-6000, USA
Interests: human activity recognition; ubiquitous computing; pervasive computing; IoT

Special Issue Information

Dear Colleagues,

The convergence of wearable technology and advanced machine learning (ML) techniques has revolutionized the field of human activity recognition (HAR). Wearable devices, equipped with various sensors, provide continuous and detailed data that, when analyzed with sophisticated ML algorithms, can accurately identify and interpret a wide range of human activities. This Special Issue, "Wearable Computing and Activity Recognition", seeks to gather pioneering research and innovative methodologies that leverage wearable technology for HAR, pushing the boundaries of what is possible in this dynamic field.

Human activity recognition is essential for numerous applications, including healthcare, fitness, security, and smart environments. The continuous data flow from wearables necessitates robust ML models to process and make sense of this information in real time. We aim to showcase research that enhances the accuracy, reliability, and practicality of HAR systems using wearable computing. We invite you to submit your latest findings and contribute to advancing this exciting area of research.

Prof. Dr. Yang Gao
Dr. Yincheng Jin
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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • sensor design and integration
  • activity recognition algorithms
  • data processing and analysis
  • applications in healthcare
  • user interfaces and interaction techniques for wearable devices
  • data privacy and security in wearable devices
  • sports and fitness
  • integration of llms with wearable devices for enhanced activity recognition
  • conversational agents and virtual assistants in wearables
  • multimodal data fusion with llms for improved activity recognition
  • lifelong/continual human activity learning
  • adaptation and generalization

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Published Papers (3 papers)

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Research

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31 pages, 469 KiB  
Article
Enhancing Cryptographic Solutions for Resource-Constrained RFID Assistive Devices: Implementing a Resource-Efficient Field Montgomery Multiplier
by Atef Ibrahim and Fayez Gebali
Computers 2025, 14(4), 135; https://doi.org/10.3390/computers14040135 - 6 Apr 2025
Viewed by 268
Abstract
Radio Frequency Identification (RFID) assistive systems, which integrate RFID devices with IoT technologies, are vital for enhancing the independence, mobility, and safety of individuals with disabilities. These systems enable applications such as RFID navigation for blind users and RFID-enabled canes that provide real-time [...] Read more.
Radio Frequency Identification (RFID) assistive systems, which integrate RFID devices with IoT technologies, are vital for enhancing the independence, mobility, and safety of individuals with disabilities. These systems enable applications such as RFID navigation for blind users and RFID-enabled canes that provide real-time location data. Central to these systems are resource-constrained RFID devices that rely on RFID tags to collect and transmit data, but their limited computational capabilities make them vulnerable to cyberattacks, jeopardizing user safety and privacy. Implementing the Elliptic Curve Cryptography (ECC) algorithm is essential to mitigate these risks; however, its high computational complexity exceeds the capabilities of these devices. The fundamental operation of ECC is finite field multiplication, which is crucial for securing data. Optimizing this operation allows ECC computations to be executed without overloading the devices’ limited resources. Traditional multiplication designs are often unsuitable for such devices due to their excessive area and energy requirements. Therefore, this work tackles these challenges by proposing an efficient and compact field multiplier design optimized for the Montgomery multiplication algorithm, a widely used method in cryptographic applications. The proposed design significantly reduces both space and energy consumption while maintaining computational performance, making it well-suited for resource-constrained environments. ASIC synthesis results demonstrate substantial improvements in key metrics, including area, power consumption, Power-Delay Product (PDP), and Area-Delay Product (ADP), highlighting the multiplier’s efficiency and practicality. This innovation enables the implementation of ECC on RFID assistive devices, enhancing their security and reliability, thereby allowing individuals with disabilities to engage with assistive technologies more safely and confidently. Full article
(This article belongs to the Special Issue Wearable Computing and Activity Recognition)
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21 pages, 714 KiB  
Article
Heart Rate Variability-Based Stress Detection and Fall Risk Monitoring During Daily Activities: A Machine Learning Approach
by Ines Belhaj Messaoud and Ornwipa Thamsuwan
Computers 2025, 14(2), 45; https://doi.org/10.3390/computers14020045 - 30 Jan 2025
Viewed by 2038
Abstract
Impaired balance and mental stress are significant health concerns, particularly among older adults. This study investigated the relationship between the heart rate variability and fall risk during daily activities among individuals over 40 years old. This aimed to explore the potential of the [...] Read more.
Impaired balance and mental stress are significant health concerns, particularly among older adults. This study investigated the relationship between the heart rate variability and fall risk during daily activities among individuals over 40 years old. This aimed to explore the potential of the heart rate variability as an indicator of stress and balance loss. Data were collected from 14 healthy participants who wore a Polar H10 heart rate monitor and performed Berg Balance Scale activities as part of an assessment of functional balance. Machine learning techniques applied to the collected data included two phases: unsupervised clustering and supervised classification. K-means clustering identified three distinct physiological states based on HRV features, such as the high-frequency band power and the root mean square of successive differences between normal heartbeats, suggesting patterns that may reflect stress levels. In the second phase, integrating the cluster labels obtained from the first phase together with HRV features into machine learning models for fall risk classification, we found that Gradient Boosting performed the best, achieving an accuracy of 95.45%, a precision of 93.10% and a recall of 85.71%. This study demonstrates the feasibility of using the heart rate variability and machine learning to monitor physiological responses associated with stress and fall risks. By highlighting this potential biomarker of autonomic health, the findings contribute to developing real-time monitoring systems that could support fall prevention efforts in everyday settings for older adults. Full article
(This article belongs to the Special Issue Wearable Computing and Activity Recognition)
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Review

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17 pages, 1195 KiB  
Review
Exploring the Design for Wearability of Wearable Devices: A Scoping Review
by Yeo Weon Seo, Valentina La Marca, Animesh Tandon, Jung-Chih Chiao and Colin K. Drummond
Computers 2024, 13(12), 326; https://doi.org/10.3390/computers13120326 - 5 Dec 2024
Cited by 2 | Viewed by 2589
Abstract
Wearable smart devices have become ubiquitous in modern society, extensively researched for their health monitoring capabilities and convenience features. However, the “wearability” of these devices remains a relatively understudied area, particularly in terms of design informed by clinical trials. Wearable devices possess significant [...] Read more.
Wearable smart devices have become ubiquitous in modern society, extensively researched for their health monitoring capabilities and convenience features. However, the “wearability” of these devices remains a relatively understudied area, particularly in terms of design informed by clinical trials. Wearable devices possess significant potential to enhance daily life, yet their success depends on understanding and validating the design factors that influence comfort, usability, and seamless integration into everyday routines. This review aimed to evaluate the “wearability” of smart devices through a mixed-methods scoping literature review. By analyzing studies on comfort, usability, and daily integration, it sought to identify design improvements and research gaps to enhance user experience and system design. From an initial pool of 130 publications (1998–2024), 19 studies met the inclusion criteria. The review identified three significant outcomes: (1) a lack of standardized assessment methods, (2) the predominance of qualitative over quantitative assessments, and (3) limited utility of findings for informing design. Although qualitative studies provide valuable insights, the absence of quantitative research hampers the development of validated, generalizable design criteria. This underscores the urgent need for future studies to adopt robust quantitative methodologies to better assess wearability and inform evidence-based design strategies. Full article
(This article belongs to the Special Issue Wearable Computing and Activity Recognition)
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