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Battery-Free Smart Textile Boosts Wireless Sensor Data Throughput 37.8 Times

*Important notice: This news reports on an unedited version of an accepted paper and is awaiting final editing. Therefore, the paper should not be regarded as conclusive or treated as established information.

A new dual-mode metamaterial textile boosts wireless data throughput by 37.8 times while doubling power-transfer efficiency compared with emerging near-field clothing. This technology supports a battery-free wireless body sensor network, enabling continuous human-machine interaction without restricting user movement. These findings were published in the journal Nature Communications.

Human hand pointing with an digital communications/AI-inspired overlay
Study: Metamaterial-enabled battery-free wireless sensor networks for unencumbered human-machine interactions. Image Credit: nepool/Shutterstock.com

Why Today’s Wireless Body Sensors Still Limit Movement

Wireless body sensor networks are crucial components of human-machine interfaces in fields like robotics and assistive healthcare, continuously collecting data on movement and physiological conditions. However, conventional systems often rely on wires or portable battery packs, which can limit natural movement and make long-term use impractical.

Wireless power transfer technology enables devices such as smartphones to serve as mobile power sources. Existing textile-integrated near-field systems typically operate in a single mode, focusing either on efficient power transfer or high-speed communication; this limitation prevents them from providing both functions simultaneously, which is essential for sensor systems that require continuous power and rapid data exchange.

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Design and Fabrication of Dual-Mode Textiles

To address these limitations, researchers developed a battery-free wireless body sensor network based on a dual-mode metamaterial textile. The textile was fabricated using computer-aided digital embroidery to integrate conductive liquid-metal fibers into clothing substrates. The fibers were created by injecting Galinstan, a conductive gallium-indium-tin alloy, into perfluoroalkoxy alkane tubing, allowing them to conform to the human body.

The metamaterial textile combines spoof surface plasmon waveguides with interconnected planar inductor patterns. This design supports 13.56 MHz near-field communication signals for wireless power transfer and 2.4 GHz Bluetooth Low Energy signals for high-speed data transmission.

By utilizing separate propagation paths for these frequency ranges, the system reduces mutual interference, establishing a robust electromagnetic pathway across the body.

Metamaterial Textile Boosts Wireless Power and Data Performance

Electromagnetic characterization and benchtop testing demonstrated improved performance when the dual-mode metamaterial architecture was

used. The textile pathway extends wireless power transfer to sensor nodes up to one meter from the transmitter while maintaining a power transfer efficiency of approximately 70%.

Compared to other near-field textile configurations, the metamaterial design significantly increased data throughput (by approximately 37.8 times).

During communication tests, the spoof surface plasmon waveguide confined the 2.4 GHz signals as localized surface waves, effectively reducing attenuation and scattering caused by biological tissues.

The system completed data transmission in 5.58 seconds at a transmit power of -40 dBm, while conventional radiative Bluetooth communication took 15.98 seconds at a higher transmit power of -20 dBm for the same tasks. Communication latency remained below 10 ms, supporting real-time data exchange during continuous sensing.

The system also maintained performance during movement and physical misalignment. The transmission coefficient remained above -50 dB for lateral displacements of up to 16 mm and vertical separations of up to 20 mm. During activities such as walking and running, the rectified voltage at multiple sensor nodes consistently exceeded the 2 V level required for stable operation, with no packet loss or communication dropouts recorded.

Battery-Free Sensors Enable Robotic Control and Virtual Interaction

The battery-free sensor network was tested in various human-machine interaction applications.

Using a customized sensory glove, researchers demonstrated real-time, closed-loop robotic teleoperation, in which a robotic arm replicated human finger movements and hand trajectories with no noticeable delay. Additionally, the sensor network enabled gesture-based interaction in virtual reality environments.

The multimodal sensors allowed users to write in the air, scroll through virtual interfaces, and resize digital objects using natural hand gestures; a machine learning model interpreting the sensor data recognized handwritten characters with 82.8% accuracy. Sensor nodes distributed across a customized garment also captured full-body movements and reconstructed human kinematics.

Toward Battery-Free, Full-Body Wireless Sensor Networks

This study demonstrates a dual-mode metamaterial textile for battery-free human-machine interfaces. By guiding low-frequency power transfer and high-frequency communication signals through separate pathways, the system reduces the need for batteries and wiring in wearable sensor networks. The network supports continuous physiological and kinematic monitoring while allowing users to move freely.

Its battery-free architecture has significant potential for sensor networks in healthcare, immersive spatial computing, and industrial robotics. Future work could focus on optimizing antenna geometries and developing on-device machine learning methods for real-time interpretation of sensor data. Further testing will be essential to assess the system's performance across various body movements and practical operating conditions.

Journal Reference

Zhang, Y., et al. (2026). Metamaterial-enabled battery-free wireless sensor networks for unencumbered human-machine interactions. Nature Communications. DOI: 10.1038/s41467-026-77600-2. https://www.nature.com/articles/s41467-026-77600-2.

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Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

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