Researchers at Dongguk University in South Korea have developed a flexible, battery-free electronic device that can harvest energy from human movement and use it to power neuromorphic sensing and learning functions.
The technology could eventually be used in wearable health-monitoring systems, electronic skin, smart prosthetics, human-machine interfaces and intelligent motion-monitoring devices without requiring conventional batteries or an external power supply.
The system reminds us of the Seiko Kinetic watch (main image), first released in 1988, which also harvested energy from the movement of the wearer.
Led by Professor Sejoon Lee of Dongguk University’s Department of System Semiconductor, the research team combined a triboelectric nanogenerator, or TENG, with a flexible graphene-channel ion-gel-gated transistor.
The TENG converts mechanical stimuli such as body movement, touch or vibration into electrical signals. These signals directly operate the artificial synaptic functions of the device, meaning it can sense movement and process information without a separate source of electrical power.
Lee says: “In human tactile perception mechanoreceptors sense even minute mechanical disturbances and convert them into neural spikes.
“To replicate this process electronically, we integrated a triboelectric nanogenerator with a g-IGT that converts mechanical stimuli into electrical signals that directly regulate artificial synaptic behavior without requiring external power.”
The researchers demonstrated several forms of artificial memory, ranging from sensory memory lasting around 70 milliseconds to short-term memory lasting 0.2-0.45 seconds.
Repeated stimulation could also move the system toward a longer-term memory state lasting more than two seconds.
The device was additionally tested using an artificial neural network designed to recognize six human activities, including walking, sitting, standing and climbing stairs.
Using the experimentally measured behavior of the device, the system achieved 88.05 percent accuracy in classifying the activities while the flexible device was under bending.
The researchers say the technology could ultimately enable wearable AI systems combining sensing, memory, learning and information processing while reducing or eliminating their dependence on batteries.
Lee says: “Our research could contribute to a new generation of wearable artificial intelligence systems that operate with minimal reliance on batteries or external computing resources.
“More broadly, our work points toward self-powered neuromorphic electronics with integrated sensing, memory, learning, and information processing in a single flexible platform.”
The research was published in Advanced Materials in July 2026.
