Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
5.2
Journals
Technologies
Special Issues
Wearable Vital Signs and Activities Detection: Systems and Applications
share announcement

Wearable Vital Signs and Activities Detection: Systems and Applications

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: closed (31 July 2026) | Viewed by 9048

Editors

Dr. Chenxi Yang

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing, China
Interests: wearable cardiovascular monitoring
Dr. Huanghe Zhang

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan, China
Interests: wearable technologies for rehabilitation
Dr. Haoyi Fan

E-Mail Website
Guest Editor
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Interests: bio-signal processing and AI-driven models; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, the detection of vital signs and activity via wearable devices is a topic of extensive research within both the academic and industrial communities. These devices are being utilized and developed for a broad array of scenarios, extending beyond the clinical environment to provide more universally accessible data for various applications. These applications include the assessment of cardiovascular systems, sleep statuses, emotions, mobility, and daily activities, among others. Despite their ongoing development, smart wearable systems present numerous challenges and opportunities for discussion, especially within the context of artificial intelligence and the Internet of Things (IoT). This Special Issue aims to explore these systems and applications, specifically focusing on the detection of wearable vital signs and activities.

Submissions should encompass, although not be confined to, the following topics:

  1. Sensors, circuits, and systems designed for the detection of wearable vital signs and activities.
  2. Signal processing methods specifically tailored to addressing the challenges inherent in wearable scenarios, including but not limited to motion artifacts and low signal-to-noise ratios.
  3. Applications developed based on the wearable detection of vital signs and/or activity.
  4. Interdisciplinary research utilizing wearable vital signs and activity detection systems

It is imperative to note that the primary focus of this Special Issue is on systems and applications designed for humans. Systems intended for laboratory animals, pets, and other non-human subjects are beyond the scope of this consideration.

Dr. Chenxi Yang
Dr. Huanghe Zhang
Dr. Haoyi Fan
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies 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

  • wearable systems
  • vital signs
  • activity monitoring
  • bio-signal processing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 19747 KB  
Article
A Contactless Edge-AI Prototype for Simulated Apnea-like Respiratory Suppression and Motion Artifact Detection Using 60 GHz FMCW Radar
by Sathit Pairoch, Pattarapong Phasukkit and Nongluck Houngkamhang
Technologies 2026, 14(7), 388; https://doi.org/10.3390/technologies14070388 - 24 Jun 2026
Viewed by 313
Abstract
Sleep-related respiratory disturbances are difficult to monitor continuously outside specialized laboratories because conventional polysomnography is resource-intensive and intrusive. This study presents a contactless edge-AI engineering prototype for detecting controlled voluntary respiratory-motion suppression and motion artifacts using a 60 GHz frequency-modulated continuous-wave radar. The [...] Read more.
Sleep-related respiratory disturbances are difficult to monitor continuously outside specialized laboratories because conventional polysomnography is resource-intensive and intrusive. This study presents a contactless edge-AI engineering prototype for detecting controlled voluntary respiratory-motion suppression and motion artifacts using a 60 GHz frequency-modulated continuous-wave radar. The system integrates a 60 GHz radar front end, lightweight local preprocessing, an INT8 one-dimensional convolutional neural network deployed on the Analog Devices MAX78000 CNN accelerator (Analog Devices Thailand, Chon Buri, Thailand), and an event-driven Raspberry Pi Zero 2W gateway for alert transmission. Evaluation was performed using a controlled healthy-volunteer dataset consisting of normal breathing, voluntary breath-holding-induced respiratory suppression, and deliberate motion artifact. The final valid test set contained 270 technically valid 30 s windows balanced across the three classes. The INT8 model achieved an overall accuracy of 92.6% (95% confidence interval: 88.8–95.2%), with a macro-averaged precision, recall, and F1-score of 92.6%, 92.6%, and 92.5%, respectively. Active CNN inference on the MAX78000 consumed 0.152 ± 0.011 mJ and was completed in 5.20 ± 0.11 ms, corresponding to approximately 280-fold lower active inference energy than Python 3.14.6/TensorFlow Lite 2.21.0-based execution on the Raspberry Pi Zero 2W. These results demonstrate the feasibility of privacy-aware, low-power respiratory-pattern classification at the edge. However, the study should be interpreted strictly as an engineering proof-of-concept based on controlled voluntary breathing and movement tasks in healthy volunteers. It is not a clinically validated apnea or obstructive sleep apnea detection system and did not include polysomnography, oxygen saturation measurement, airflow sensing, sleep staging, or diagnosed patient cohorts. Full article
(This article belongs to the Special Issue Wearable Vital Signs and Activities Detection: Systems and Applications)
►▼ Show Figures

Graphical abstract

22 pages, 1876 KB  
Article
Vocal-Eyes: AI-Powered Smart Glasses for the Blind Using Transformer-Based Architecture and Scene Graph Generation
by Amna Shabbir, Uzma Afsheen, Muhammad Faizan Shirazi, Abdul Rauf, Syed Muhammad Meesam Abbas, Shahid Saeed, Abdul Samad Khan, Safdar Rizvi and Nurashikin Saaludin
Technologies 2026, 14(7), 384; https://doi.org/10.3390/technologies14070384 - 24 Jun 2026
Viewed by 593
Abstract
Visually impaired individuals face significant challenges in autonomous mobility and situational awareness. Most existing assistive technologies address isolated tasks, such as object recognition or text reading, while failing to capture broader environmental context. This work addresses this limitation by proposing a scene-sensitive, low-cost [...] Read more.
Visually impaired individuals face significant challenges in autonomous mobility and situational awareness. Most existing assistive technologies address isolated tasks, such as object recognition or text reading, while failing to capture broader environmental context. This work addresses this limitation by proposing a scene-sensitive, low-cost assistive system that delivers holistic situational information. We present Vocal-Eyes, an intelligent smart glasses platform that provides periodic audio descriptions of the surrounding environment. The system employs a cloud-based neural processing pipeline in which visual features are extracted using a Transformer-based architecture. Relational context is modeled through scene graph generation, and scene graphs are translated into natural language via a graph-to-text module. A lightweight hardware prototype captures visual data locally, while computationally intensive processing is offloaded to the cloud to reduce power consumption. The experimental results show that relational, scene-based narration produces more coherent and informative descriptions than object-centric approaches while maintaining acceptable periodic latency. Cost analysis further indicates that Vocal-Eyes is significantly more affordable than comparable commercial smart glasses solutions. These results demonstrate that Transformer-based scene understanding with cloud-assisted processing is an effective and practical approach for developing accessible, context-aware assistive technologies for visually impaired users. Full article
(This article belongs to the Special Issue Wearable Vital Signs and Activities Detection: Systems and Applications)
►▼ Show Figures

Figure 1

15 pages, 393 KB  
Article
A Benchmarking Framework for Cost-Effective Wearables in Oncology: Supporting Remote Monitoring and Scalable Digital Health Integration
by Bianca Bindi, Marina Garofano, Chiara Parretti, Claudio Pascarelli, Gabriele Arcidiacono, Romeo Bandinelli and Angelo Corallo
Technologies 2026, 14(1), 24; https://doi.org/10.3390/technologies14010024 - 1 Jan 2026
Cited by 2 | Viewed by 1789
Abstract
Wearable technologies are increasingly integrated into digital health systems to support continuous remote monitoring in oncology; however, the lack of standardized and reproducible criteria for device selection limits their scalable and regulation-compliant adoption in clinically oriented infrastructures. This study proposes a preclinical benchmarking [...] Read more.
Wearable technologies are increasingly integrated into digital health systems to support continuous remote monitoring in oncology; however, the lack of standardized and reproducible criteria for device selection limits their scalable and regulation-compliant adoption in clinically oriented infrastructures. This study proposes a preclinical benchmarking framework for the systematic evaluation of commercially available wearable devices for oncology applications. Devices were assessed across six predefined dimensions: biometric data acquisition, application programming interface-based interoperability, regulatory compliance, battery autonomy, cost, and absence of mandatory subscription fees. From an initial pool of 23 devices, a stepwise screening process identified 6 eligible wearables, which were compared using a semi-quantitative weighted scoring system. The benchmarking analysis identified the Withings ScanWatch 2 as the highest-ranked device, achieving a score of 37/40 and representing the only solution combining medical-grade certification for selected functions, extended battery life (up to 30 days), declared General Data Protection Regulation-compliant data governance, and fully accessible application programming interfaces. The remaining devices scored between 17 and 23 due to limitations in certification, battery autonomy, or data accessibility. This work introduces a reproducible preclinical benchmarking methodology that supports transparent wearable device selection in oncology and provides a foundation for future scalable digital health integration under appropriate regulatory and interoperability governance. Full article
(This article belongs to the Special Issue Wearable Vital Signs and Activities Detection: Systems and Applications)
►▼ Show Figures

Graphical abstract

24 pages, 9349 KB  
Article
Enhanced Pedestrian Navigation with Wearable IMU: Forward–Backward Navigation and RTS Smoothing Techniques
by Yilei Shen, Yiqing Yao, Chenxi Yang and Xiang Xu
Technologies 2025, 13(7), 296; https://doi.org/10.3390/technologies13070296 - 9 Jul 2025
Cited by 2 | Viewed by 2322
Abstract
Accurate and reliable pedestrian positioning service is essential for providing Indoor Location-Based Services (ILBSs). Zero-Velocity Update (ZUPT)-aided Strapdown Inertial Navigation System (SINS) based on foot-mounted wearable Inertial Measurement Units (IMUs) has shown great performance in pedestrian navigation systems. Though the velocity errors will [...] Read more.
Accurate and reliable pedestrian positioning service is essential for providing Indoor Location-Based Services (ILBSs). Zero-Velocity Update (ZUPT)-aided Strapdown Inertial Navigation System (SINS) based on foot-mounted wearable Inertial Measurement Units (IMUs) has shown great performance in pedestrian navigation systems. Though the velocity errors will be corrected once zero-velocity measurement is available, the navigation system errors accumulated during measurement outages are yet to be further optimized by utilizing historical data during both stance and swing phases of pedestrian gait. Thus, in this paper, a novel Forward–Backward navigation and Rauch–Tung–Striebel smoothing (FB-RTS) navigation scheme is proposed. First, to efficiently re-estimate past system state and reduce accumulated navigation error once zero-velocity measurement is available, both the forward and backward integration method and the corresponding error equations are constructed. Second, to further improve navigation accuracy and reliability by exploiting historical observation information, both backward and forward RTS algorithms are established, where the system model and observation model are built under the output correction mode. Finally, both navigation results are combined to achieve the final estimation of attitude and velocity, where the position is recalculated by the optimized data. Through simulation experiments and two sets of field tests, the FB-RTS algorithm demonstrated superior performance in reducing navigation errors and smoothing pedestrian trajectories compared to traditional ZUPT method and both the FB and the RTS method, whose advantage becomes more pronounced over longer navigation periods than the traditional methods, offering a robust solution for positioning applications in smart buildings, indoor wayfinding, and emergency response operations. Full article
(This article belongs to the Special Issue Wearable Vital Signs and Activities Detection: Systems and Applications)
►▼ Show Figures

Figure 1

14 pages, 3134 KB  
Article
Development of a Low-Cost Multi-Physiological Signal Simulation System for Multimodal Wearable Device Calibration
by Tumenkhuslen Delgerkhaan, Qun Wei, Jiwoo Jung, Sangwon Lee, Gangoh Na, Bongjo Kim, In-Cheol Kim and Heejoon Park
Technologies 2025, 13(6), 239; https://doi.org/10.3390/technologies13060239 - 10 Jun 2025
Viewed by 2764
Abstract
Using multimodal wearable devices to diagnose cardiovascular diseases early is essential for providing timely medical assistance, particularly in remote areas. This approach helps prevent risks and reduce mortality rates. However, prolonged use of medical devices can lead to measurement inaccuracies, necessitating calibration to [...] Read more.
Using multimodal wearable devices to diagnose cardiovascular diseases early is essential for providing timely medical assistance, particularly in remote areas. This approach helps prevent risks and reduce mortality rates. However, prolonged use of medical devices can lead to measurement inaccuracies, necessitating calibration to maintain precision. Unfortunately, wearable devices often lack affordable calibrators that are suitable for personal use. This study introduces a low-cost simulation system for phonocardiography (PCG) and photoplethysmography (PPG) signals designed for a multimodal smart stethoscope calibration. The proposed system was developed using a multicore microprocessor (MCU), two digital-to-analog converters (DACs), an LED light, and a speaker. It synchronizes dual signals by assigning tasks based on a multitasking function. A designed time adjustment algorithm controls the pulse transit time (PTT) to simulate various cardiovascular conditions. The simulation signals are generated from preprocessed PCG and PPG signals collected during in vivo experiments. A prototype device was manufactured to evaluate performance by measuring the generated signal using an oscilloscope and a multimodal smart stethoscope. The preprocessed signals, generated signals, and measurements by the smart stethoscope were compared and evaluated through correlation analysis. The experimental results confirm that the proposed system accurately generates the features of the physiological signals and remains in phase with the original signals. Full article
(This article belongs to the Special Issue Wearable Vital Signs and Activities Detection: Systems and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop