Next-Generation Edge AI in Wearable Devices
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 1932
Special Issue Editors
Interests: AI intelligence to flexible materials; intelligent embedded systems (IoT); tribo electric nanogenerators (TENGs); nano sensors; advanced nanocomposite materials
Special Issues, Collections and Topics in MDPI journals
Interests: smart textiles and intelligent clothing systems; digital manufacturing; fiber and polymer technologies; AI-enabled fabric design; wearable sensor integration
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The convergence of Edge Artificial Intelligence (Edge AI) and wearable sensor technologies is transforming personalized healthcare, human–machine interaction, and ubiquitous sensing. Unlike cloud-based systems, Edge AI-enabled wearables can process data locally, reducing latency, preserving privacy, and enabling real-time adaptive responses. This Special Issue invites contributions exploring next-generation intelligent wearables powered by embedded AI algorithms, neuromorphic computing, and ultra-low-power architectures. Submissions may focus on adaptive signal processing, context-aware intelligence, and secure integration into IoT frameworks. The issue also welcomes research on bio-integrated electronics and intelligent devices that merge advanced electronics with computational intelligence. Bringing together experts from electronics, computing, and AI, this Special Issue highlights how Edge AI transforms wearable platforms such as smartwatches, wristbands, glasses, necklaces, head caps, and other innovative devices into intelligent, adaptive, and resilient companions, advancing the mission of Sensors to promote innovation across connected sensing ecosystems.
Scope & Topics
Submissions encouraged include, but are not limited to, the following areas:
- Edge AI architectures for wearable sensors.
- Machine learning and adaptive signal processing for physiological, environmental, or activity monitoring.
- Flexible and adaptive electronic systems for wearables.
- Secure and privacy-aware wearable IoT frameworks.
- Neuromorphic and bio-inspired computing at the edge.
- Real-time analytics for health, sports, and environmental applications.
- Design, prototyping, and scalability of wearable platforms.
Dr. Akshaya Aliyana
Prof. Dr. George K. Stylios
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 2600 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
- edge AI
- wearable sensors
- smart textiles
- IoT
- neuromorphic computing
- flexible electronics
- energy harvesting
- real-time analytics
- privacy-preserving sensing
- adaptive learning systems
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