A dual-mode metamaterial textile boosts wireless data and power transfer, enabling battery-free wearable sensors for robotics, healthcare, virtual reality, and human-machine interaction without battery packs.
Researchers at Tsinghua University, Shenzhen MSU-BIT University, and partner institutions have developed a dual-mode metamaterial textile that forms a battery-free wireless body sensor network. The textile acts as the core wearable platform, allowing distributed sensors to receive power and transmit data without individual batteries.
The system combines two electromagnetic pathways: 13.56MHz near-field wireless power transfer and 2.4GHz Bluetooth Low Energy communication. Researchers digitally embroidered liquid-metal fibres into clothing to create conductive patterns that guide electromagnetic signals around the body. An NFC-enabled smartphone can serve as the central hub, supplying power to distributed sensors while collecting multimodal signals.
The architecture increased wireless data throughput by 37.8 times and doubled wireless power-transfer efficiency compared with emerging near-field clothing systems. The textile can transfer power to sensor nodes positioned up to one metre from the transmitter while maintaining approximately 70% power-transfer efficiency. Communication latency remained below 10ms during testing.
The network was also evaluated during movement and physical misalignment. Sensor nodes maintained stable operation during activities such as walking and running, helping reduce restrictions associated with cables and wearable battery packs.
Researchers demonstrated the technology in several human-machine interaction applications. A customised sensory glove enabled real-time robotic teleoperation, allowing a robotic arm to follow human finger movements and hand trajectories. The system also supported gesture-based interaction in virtual reality, including writing in the air, scrolling through interfaces, and manipulating digital objects. A machine-learning model recognised handwritten characters with 82.8% accuracy.
The technology could support battery-free wearable sensing for robotics, healthcare, immersive computing, and human digital twins. The research was published in Nature Communications on 14 September 2026.
