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Wearable Electronics and Self-Powered Sensors

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

Deadline for manuscript submissions: 25 September 2025 | Viewed by 855

Special Issue Editors


E-Mail Website1 Website2
Guest Editor
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, China
Interests: energy harvesting; self-powered systems and active sensors; nanogenerators

E-Mail Website
Guest Editor
1. Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
2. School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
Interests: nanogenerators; energy harvesting; energy storage
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Special Issue Information

Dear Colleagues,

The advancement of the Internet of Things (IoT), big data, and artificial intelligence depends on a broadly distributed sensing network, driven by energy storage units that have finite lifespans and environmental impacts. The wide distribution and high mobility of these sensors necessitate renewable, distributed energy sources to ensure the success of the IoT and sustainable human development. Mechanical energy has emerged as the most abundant renewable resource in various environments and is unaffected by weather conditions. This Special Issue, “Wearable Electronics and Self-Powered Sensors”, explores harvesting ambient mechanical energy (e.g., body motion) to create battery-free, autonomous systems. Core technologies, such as Triboelectric Generators (TENGs) and Piezoelectric Generators (PENGs), enable intrinsically self-powered sensing for physiological, biochemical, and environmental parameters. Innovations in flexible materials and fabrication facilitate wearable integrated technologies (patches, e-textiles). Key challenges include system integration (harvesters, power management, sensors, ultra-low-power electronics, and wireless communications like BLE/NFC) into robust, autonomous packages. Addressing stability, biocompatibility, and scalable manufacturing is crucial for deployment. The Special Issue highlights self-powered technology's potential to enable pervasive, sustainable wearable and IoT sensing networks, which are essential for future digitalization and societal progress.

Yours faithfully,

Dr. Yang Jiang
Dr. Jianjun Luo
Guest Editors

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Keywords

  • wearable sensors
  • wearable electronics
  • self-powered systems
  • self-powered sensing
  • triboelectric generators
  • piezoelectric generators
  • blue energy
  • high-voltage applications
  • nanomaterials

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Published Papers (1 paper)

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Research

14 pages, 2463 KB  
Article
Gesture-Based Secure Authentication System Using Triboelectric Nanogenerator Sensors
by Doohyun Han, Kun Kim, Jaehee Shin and Jinhyoung Park
Sensors 2025, 25(16), 5170; https://doi.org/10.3390/s25165170 - 20 Aug 2025
Viewed by 442
Abstract
This study presents a gesture-based authentication system utilizing triboelectric nanogenerator (TENG) sensors. As self-powered devices capable of generating high-voltage outputs without external power, TENG sensors are well-suited for low-power IoT sensors and smart device applications. The proposed system recognizes single tap, double tap, [...] Read more.
This study presents a gesture-based authentication system utilizing triboelectric nanogenerator (TENG) sensors. As self-powered devices capable of generating high-voltage outputs without external power, TENG sensors are well-suited for low-power IoT sensors and smart device applications. The proposed system recognizes single tap, double tap, and holding gestures. The electrical characteristics of the sensor were evaluated under varying pressure conditions, confirming a linear relationship between applied force and output voltage. These results demonstrate the sensor’s high sensitivity and precision. A threshold-based classification algorithm was developed by analyzing signal features enabling accurate gesture recognition in real time. To enhance the practicality and scalability of the system, the algorithm was further configured to automatically segment raw sensor signals into gesture intervals and assign corresponding labels. From these segments, time-domain statistical features were extracted to construct a training dataset. A random forest classifier trained on this dataset achieved a high classification accuracy of 98.15% using five-fold cross-validation. The system reduces security risks commonly associated with traditional keypad input, offering a user-friendly and reliable authentication interface. This work confirms the feasibility of TENG-based gesture recognition for smart locks, IoT authentication devices, and wearable electronics, with future improvements expected through AI-based signal processing and multi-sensor integration. Full article
(This article belongs to the Special Issue Wearable Electronics and Self-Powered Sensors)
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