Nature Sensors<\/a>, 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>\nWearable technologies with gesture sensors work fine when a user is sitting still, but the signals start to fall apart under excessive motion noise, explained study co-first author Xiangjun Chen, a postdoctoral researcher in the Aiiso Yufeng Li Family Department of Chemical and Nano Engineering at the UC San Diego Jacobs School of Engineering. This limits their practicality in daily life. \u201cOur system overcomes this limitation,\u201d Chen said. \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
The technology could enable patients in rehabilitation or individuals with limited mobility, for example, to use natural gestures to control robotic aids without relying on fine motor skills. Industrial workers and first responders could potentially use the technology for hands-free control of tools and robots in high-motion or hazardous environments. It could even enable divers and remote operators to command underwater robots despite turbulent conditions. In consumer devices, the system could make gesture-based controls more reliable in everyday settings.<\/p>\n
The work was a collaboration between the labs of Sheng Xu and Joseph Wang, both professors in the Aiiso Yufeng Li Family Department of Chemical and Nano Engineering at the UC San Diego Jacobs School of Engineering.<\/p>\n
To the researchers\u2019 knowledge, this is the first wearable human-machine interface that works reliably across a wide range of motion disturbances. As a result, it can work with the way people actually move.<\/p>\n
The 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. 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 \u2014 such as a robotic arm \u2014 in real time.<\/p>\n
\u201cThis advancement brings us closer to intuitive and robust human-machine interfaces that can be deployed in daily life,\u201d Chen said.<\/p>\n
The system was tested in multiple dynamic conditions. Subjects used the device to control a robotic arm while running, exposed to high-frequency vibrations, and under a combination of disturbances. The device was also validated under simulated ocean conditions using the Scripps Ocean-Atmosphere Research Simulator at UC San Diego\u2019s Scripps Institution of Oceanography, which recreated both lab-generated and real sea motion. In all cases, the system delivered accurate, low-latency performance.<\/p>\n
Originally, this project was inspired by the idea of helping military divers control underwater robots. But the team soon realized that interference from motion wasn\u2019t just a problem unique to underwater environments. It is a common challenge across the field of wearable technology, one that has long limited the performance of such systems in everyday life.<\/p>\n
\u201cThis work establishes a new method for noise tolerance in wearable sensors,\u201d Chen said. \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
Full study: \u201cA noise-tolerant human-machine interface based on deep learning-enhanced wearable sensors<\/a>.\u201d Co-first authors on the study are UC San Diego researchers Xiangjun Chen, Zhiyuan Lou, Xiaoxiang Gao and Lu Yin.<\/p>\nThis work was supported by the Defense Advanced Research Projects Agency (DARPA, contract number HR001120C0093).<\/p>\n","protected":false},"excerpt":{"rendered":"Engineers at the University of California San Diego have developed a next-generation wearable system that enables people to…\n","protected":false},"author":2,"featured_media":138709,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[85,46,22129,983,125,24616,86094],"class_list":["post-138708","post","type-post","status-publish","format-standard","has-post-thumbnail","category-technology","tag-il","tag-israel","tag-nature-journal","tag-newswise","tag-technology","tag-university-of-california-san-diego","tag-wearable-deviceswearable-technologyhuman-machine-interfaces"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts\/138708","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/comments?post=138708"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts\/138708\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/media\/138709"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/media?parent=138708"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/categories?post=138708"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/tags?post=138708"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}