AI-powered wearable turns everyday gestures into machine control

Researchers at the University of California San Diego have developed an AI-powered wearable system that accurately interprets natural arm gestures to control machines, even under intense motion disturbances such as running, riding in a car, or turbulent ocean conditions. This next-generation human–machine interface combines soft, stretchable sensors embedded in a thin electronic patch with a deep-learning model that filters out motion noise in real time, enabling reliable gesture recognition in real-world, high-motion environments. Unlike previous wearable gesture sensors that fail with excessive movement, this system maintains accuracy across a broad range of disturbances, making it suitable for diverse applications from medical rehabilitation to underwater robotics. The wearable device integrates motion and muscle sensors, a Bluetooth microcontroller, and a stretchable battery into a multilayered armband patch. It was rigorously tested in extreme scenarios, including simulated ocean conditions, where it demonstrated low-latency, precise control of a robotic arm despite disruptive motions. Originally inspired by military divers’ need for underwater robot control, the technology’s