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Electronics
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Innovation in AI-Based Wearable Devices
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Innovation in AI-Based Wearable Devices

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 November 2026 | Viewed by 896

Special Issue Editor

Dr. Albert Sabban

E-Mail Website
Guest Editor
Electrical Engineering Department, Braude Academic College, Karmiel 2161002, Israel
Interests: wearable systems and antennas; communication systems; medical devices and applications; system engineering; microwave technologies; wearable IoT and medical devices; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) Techniques have generated significant changes in many Activities of Daily Living (ADL). It is vital to integrate AI technologies into wearable devices, which are accessories that can comfortably be worn on the body. In medical applications, AI technologies are used in the diagnosis of medical activities and tasks, as well as in drug discovery. Wearable AI-based devices may empower data-driven decision-making, automate tasks, and personalise daily activities and experiences, improving efficiency and precision. Natural language understanding and computer vision technologies based on AI technologies revolutionise communication and visual data interpretation.

Wearable AI-based medical systems and sensors can measure body temperature, heartbeat, blood pressure, sweat rate and other physiological parameters of the person wearing the medical device. Wearable AI-based devices may provide efficient scanning and sensing features not offered by mobile phones and laptop computers. One of the main goals of wearable AI-based medical systems may be to increase disease prevention. By using more wearable AI-based medical devices, a person can better manage and be aware of their personal health.

Topics and Applications of Wearable AI-based Medical Systems:

  • Wearable medical AI-based devices may help to monitor hospital activities;
  • Wearable medical AI-based devices may assist diabetes and asthma patients;
  • Wearable AI-based devices may assist in solving obesity problems;
  • Wearable medical AI-based devices may assist in solving cardiovascular diseases;
  • Wearable medical AI-based devices may help to gather data for clinical research trials and academic research studies;
  • Wearable AI-based fitness tracker with sensors for ADL activities.

Dr. Albert Sabban
Guest Editor

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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 2400 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 AI-based systems
  • AI-based IoT devices
  • AI-based medical applications
  • AI technologies
  • AI-based communication systems

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Published Papers (1 paper)

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Research

16 pages, 1597 KB  
Article
Tiny Machine Learning Implementation for a Textile-Integrated Breath Rate Sensor
by Kenneth Egwu, Rudolf Heer, Ferenc Ender and Georgios Kokkinis
Electronics 2026, 15(8), 1646; https://doi.org/10.3390/electronics15081646 - 15 Apr 2026
Viewed by 412
Abstract
Respiratory rate (RR) is a critical indicator of physiological status, yet unobtrusive and continuous RR monitoring remains challenging, particularly in wearable applications that require soft, lightweight, and low-power sensing systems. This paper presents an integrated approach that combines a textile-embedded embroidered strain-gauge sensor [...] Read more.
Respiratory rate (RR) is a critical indicator of physiological status, yet unobtrusive and continuous RR monitoring remains challenging, particularly in wearable applications that require soft, lightweight, and low-power sensing systems. This paper presents an integrated approach that combines a textile-embedded embroidered strain-gauge sensor with Tiny Machine Learning (TinyML) to enable real-time, on-device RR estimation. The sensing platform consists of a textile-integrated meander-pattern strain gauge and a fabric-mounted analog readout circuit, which together capture thoracic expansion during breathing. Two lightweight neural network models—a convolutional neural network (CNN) operating on raw respiratory waveforms and a dense neural network (DNN) operating on wavelet features—were developed and trained using a public strain-sensor dataset and a custom dataset collected with the textile system (TexHype dataset). Both models were optimized through 8-bit quantization and deployed to an STM32L4 microcontroller, where end-to-end on-device preprocessing, filtering, segmentation, normalization, and inference were performed. The CNN achieved the highest accuracy, with a mean absolute error (MAE) of 1.23 breaths per minute (BPM) on the TexHype dataset, but exhibited substantial inference latency (5.8–6.2 s) due to its computational complexity. In contrast, the wavelet-based DNN demonstrated lower accuracy (MAE 2.21 BPM) but achieved real-time performance with inference times of 18–96 ms, and a power overhead (ΔP=Pactive−Pidle) of approximately 3.3 mW during inference. Cross-dataset testing revealed limited generalization between different strain-sensor platforms. The findings highlight key trade-offs between accuracy, latency, and energy efficiency, and illustrate the potential of combining stretchable electronics with embedded intelligence to enable next-generation wearable respiratory monitoring systems. Full article
(This article belongs to the Special Issue Innovation in AI-Based Wearable Devices)
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