{"685070":{"#nid":"685070","#data":{"type":"news","title":"The Robotic Breakthrough That Could Help Stroke Survivors Reclaim Their Stride","body":[{"value":"\u003Cp\u003ECrossing a room shouldn\u2019t feel like a marathon. But for many stroke survivors, even the smallest number of steps carries enormous weight. Each movement becomes a reminder of lost coordination, muscle weakness, and physical vulnerability.\u003C\/p\u003E\u003Cp\u003EA team of Georgia Tech researchers wanted to ease that struggle, and robotic exoskeletons offered a promising path. Their findings point to a simple but powerful shift: exoskeletons that adapt to people, rather than forcing people to adapt to the machine. Using artificial intelligence (AI) to learn the rhythm of patients\u2019 strides in real time, the team showed how these devices can reduce strain and increase efficiency. They also demonstrated how the technology can help restore confidence for stroke survivors.\u0026nbsp;\u003Cbr\u003E\u003Cbr\u003E\u003Cstrong\u003EThe Robot Finds the Rhythm\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EA robotic exoskeleton is a wearable device that helps people move with mechanical support. Traditional exoskeletons require endless manual adjustments \u2014 turning knobs, calibrating settings, and tweaking controls.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cIt can be frustrating, even nearly impossible, to get it right for each person,\u201d said \u003Ca href=\u0022https:\/\/www.me.gatech.edu\/faculty\/young\u0022\u003EAaron Young\u003C\/a\u003E, associate professor in the \u003Ca href=\u0022https:\/\/www.me.gatech.edu\/\u0022\u003EGeorge W. Woodruff School of Mechanical Engineering.\u003C\/a\u003E \u201cWith AI, the exoskeleton figures out the mapping itself. It learns the timing of someone\u2019s gait through a neural network, without an engineer needing to hand-tune everything.\u201d\u003C\/p\u003E\u003Cp\u003EThe software monitors each step, instantly updates, and fine-tunes the support it provides. Over time, the exoskeleton aligns its movements with the unique gait of the person wearing it. In this study, the research team used a hip exoskeleton, which provides torque at the hip joint \u2014 in other words, adding power to help stroke survivors walk or move their legs more easily.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETaking Smarter Steps\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EWalking after a stroke can be tough and unpredictable. A patient\u2019s stride can change from one day to the next, and even from one step to the next. Most exoskeletons aren\u2019t built for that kind of variation. They are designed around the steady, even gait of healthy young adults, which can leave stroke survivors feeling more unsteady than supported.\u003C\/p\u003E\u003Cp\u003EYoung\u2019s breakthrough, detailed in \u003Ca href=\u0022https:\/\/ieeexplore.ieee.org\/abstract\/document\/11112638\u0022\u003E\u003Cem\u003EIEEE Transactions on Robotics\u003C\/em\u003E,\u003C\/a\u003E is a neural network \u2014 a type of AI that learns patterns much like the human brain does. Sensors at the hip pick up how someone is moving, and the network translates those signals into just the right boost of power to support each step. It quickly figures out a person\u2019s unique walking pattern. But lead clinician Kinsey Herrin said the AI\u2019s learning doesn\u2019t stop there. It keeps adjusting as the patient walks, so the exoskeleton can stay in sync even during stride shifts.\u003C\/p\u003E\u003Cp\u003E\u201cThe speed really surprised us,\u201d Young said. \u201cIn just one to two minutes of walking, the system had already learned a person\u2019s gait pattern with high accuracy. That\u2019s a big deal, to adapt that quickly and then keep adapting as they move.\u201d\u003C\/p\u003E\u003Cp\u003ETests showed the system was far more accurate than the standard exoskeleton. It reduced errors in tracking stroke patients\u2019 walking patterns by 70%.\u003C\/p\u003E\u003Cp\u003EYoung emphasized that this research is about more than metrics. \u201cWhen you see someone able to walk farther without becoming exhausted, that\u2019s when you realize this isn\u2019t just about robotics \u2014 it\u2019s about giving people back a measure of independence,\u201d he said.\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAdapting Anywhere\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EEvery exoskeleton comes with its own set of sensors, so the data they collect can look completely different from one device to the next. A neural network trained on one machine often stumbles when it\u2019s moved to another. To get around that, Young\u2019s team designed software that works like a universal adapter plug \u2014 no matter what device it\u2019s connected to, it converts the signals into a form the AI can use. After just 10 strides of calibration, the system cut error rates by more than 75%.\u003C\/p\u003E\u003Cp\u003E\u201cThe goal is that someone could strap on a device, and, within a minute, it feels like it was built just for them,\u201d Young said.\u003Cbr\u003E\u003Cbr\u003E\u003Cbr\u003E\u003Cstrong\u003EA Step Toward the Future\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EWhile the study centered on stroke survivors, the implications are far broader. The same adaptive approach could support older adults coping with age-related muscle weakness, people with conditions like Parkinson\u2019s or osteoarthritis, or even children with neurological disabilities.\u0026nbsp;\u003Cbr\u003EYoung and his team are now running clinical trials to measure how well the AI-powered exoskeleton supports people in a wide range of everyday activities.\u003C\/p\u003E\u003Cp\u003E\u201cThere\u2019s no such thing as an \u2018average\u2019 user,\u201d Young said. \u201cThe real challenge is designing technology that can adapt to the full spectrum of human mobility.\u201d\u003C\/p\u003E\u003Cp\u003EIf Georgia Tech\u2019s exoskeleton can rise to that challenge, the promise goes well beyond the lab. It could mean a world where technology doesn\u2019t just help people walk \u2014 it learns to walk with them.\u003C\/p\u003E\u003Cp\u003EInseung Kang, who holds a B.S., M.S., and Ph.D. from Georgia Tech, is the paper\u2019s lead author and now an assistant professor of mechanical engineering at Carnegie Mellon University. He explained that the real promise is in what comes next.\u0026nbsp;\u003Cbr\u003E\u003Cbr\u003E\u201cWe\u2019ve developed a system that can adjust to a person\u2019s walking style in just minutes. But the potential is even greater. Imagine an exoskeleton that keeps learning with you over your lifetime, adjusting as your body and mobility change. Think of it as a robot companion that understands how you walk and gives you the right assistance every step of the way.\u201d\u003Cbr\u003E\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EAaron Young is affiliated with Georgia Tech\u2019s\u0026nbsp;\u003C\/em\u003E\u003Ca href=\u0022https:\/\/research.gatech.edu\/robotics\u0022\u003E\u003Cem\u003EInstitute for Robotics and Intelligent Machines\u003C\/em\u003E\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EThis research was primarily funded by a grant (DP2HD111709-01)\u0026nbsp;from the National Institutes of Health New Innovator Award Program. \u003C\/em\u003EGeorgia Tech researchers have created the first lung-on-a-chip with a functioning immune system, allowing it to respond to infections much like a real human lung. The breakthrough, published in \u003Cem\u003ENature Biomedical Engineering\u003C\/em\u003E, provides a more accurate way to study diseases, test therapies, and reduce reliance on animal models. With potential applications in conditions from influenza to cancer, the technology opens the door to personalized medicine that predicts how individual patients will respond to treatment.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeorgia Tech researchers have developed an AI-powered hip exoskeleton that adapts in real time to a stroke survivor\u2019s changing gait, reducing errors by 70% and helping patients walk with greater ease and confidence. Unlike traditional devices that require constant manual tuning, the system learns each person\u2019s unique stride within minutes and continues adjusting as they move. The breakthrough could extend beyond stroke recovery, offering personalized mobility support for people of all ages and conditions.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Georgia Tech\u0027s AI-fueled exoskeleton adapts to every step, helping patients relearn to walk with less effort and more confidence."}],"uid":"36410","created_gmt":"2025-09-18 15:26:54","changed_gmt":"2025-09-24 15:08:59","author":"mazriel3","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2025-09-18T00:00:00-04:00","iso_date":"2025-09-18T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"678071":{"id":"678071","type":"video","title":"The Robotic Breakthrough That Could Help Stroke Survivors Reclaim Their Stride","body":"\u003Cp\u003EGeorgia Tech\u0027s AI-fueled exoskeleton adapts to every step, helping patients relearn to walk with less effort and more confidence.\r\n\r\nTraditional robotic exoskeleton models require extensive manual calibration, but Aaron Young, associate professor in the George W. Woodruff School of Mechanical Engineering, and his team developed AI-driven software that automatically adapts to each user\u2019s gait. By using a neural network, the system continuously monitors and adjusts support with each step, gradually syncing with the wearer\u2019s unique movement. In this study, the team used a hip exoskeleton that delivers torque at the hip joint to help stroke survivors walk more easily.\u003C\/p\u003E","created":"1758208325","gmt_created":"2025-09-18 15:12:05","changed":"1758208325","gmt_changed":"2025-09-18 15:12:05","video":{"youtube_id":"RPHz2mU9sBA","video_url":"https:\/\/youtu.be\/RPHz2mU9sBA"}}},"media_ids":["678071"],"groups":[{"id":"66220","name":"Neuro"},{"id":"1188","name":"Research Horizons"}],"categories":[{"id":"138","name":"Biotechnology, Health, Bioengineering, Genetics"},{"id":"152","name":"Robotics"}],"keywords":[{"id":"194701","name":"go-resarchnews"},{"id":"13169","name":"autonomous robots"},{"id":"98751","name":"College of Engineering; George W. Woodruff School of Mechanical Engineering"},{"id":"172970","name":"go-neuro"}],"core_research_areas":[{"id":"39441","name":"Bioengineering and Bioscience"},{"id":"39521","name":"Robotics"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EMichelle Azriel Sr. Writer - Editor\u003C\/p\u003E","format":"limited_html"}],"email":["mazriel3@gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}