{"id":401279,"date":"2026-01-10T21:50:19","date_gmt":"2026-01-10T21:50:19","guid":{"rendered":"https:\/\/www.newsbeep.com\/ca\/401279\/"},"modified":"2026-01-10T21:50:19","modified_gmt":"2026-01-10T21:50:19","slug":"wearable-device-can-control-machines-while-in-motion","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ca\/401279\/","title":{"rendered":"Wearable Device Can Control Machines While in Motion"},"content":{"rendered":"

SAN DIEGO\u2014Engineers at the University of California San Diego have developed a next-generation wearable device<\/a> that enables people to control robots and other machines using everyday gestures. It combines stretchable electronics with artificial intelligence to overcome a long-standing challenge in wearable technology: reliable recognition of gesture signals in real-world environments.<\/p>\n

Wearable technologies with gesture sensors work fine when a user is sitting still, but the signals often start to become distorted with excessive motion noise.<\/p>\n

The new device is a soft electronic patch that is glued onto a cloth armband. It integrates motion and muscle sensors, a Bluetooth microcontroller and a stretchable battery into a compact, multilayered system.<\/p>\n

The system was trained from a composite dataset of real gestures and conditions, from running and shaking to the movement of ocean waves. Signals from the arm are captured and processed by a customized deep-learning framework that strips away interference, interprets the gesture, and transmits a command to control a machine\u2014such as a robotic arm\u2014in real time.<\/p>\n

\u201cThis advancement brings us closer to intuitive and robust human-machine interfaces that can be deployed in daily life,\u201d says Xiangjun Chen, Ph.D., a postdoctoral researcher working on the project. \u201cBy integrating AI to clean noisy sensor data in real time, the technology enables everyday gestures to reliably control machines even in highly dynamic environments.\u201d<\/p>\n

People used the wearable device to control a robotic arm while running, exposed to high-frequency vibrations and under a combination of disturbances.<\/p>\n

\u201cThis work establishes a new method for noise tolerance in wearable sensors,\u201d claims Chen. \u201cIt paves the way for next-generation wearable systems that are not only stretchable and wireless, but also capable of learning from complex environments and individual users.\u201d<\/p>\n

According to Chen, industrial workers and first responders could potentially use the technology for hands-free control of tools and robots in high-motion or hazardous environments. \u201cIt could even enable divers and remote operators to command underwater robots despite turbulent conditions,\u201d he points out. \u201cIn consumer devices, the system could make gesture-based controls more reliable in everyday settings.\u201d<\/p>\n


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