Engineers at Michigan State University developed a soft, flexible wearable sensor platform that improved capture of the body’s electrical signals, which they say will help artificial intelligence systems more accurately interpret movement and control prosthetic devices.
Most existing wearable sensors rely on rigid electrodes that struggle to maintain stable contact with aging skin because changes in skin, including wrinkles, thinning, and dryness, can significantly impact the contact and data quality of wearable devices. That poor connection weakens electrical signals, introduces noise, and reduces the accuracy of systems designed to interpret muscle activity.
AdapSkin uses soft, stretchable electronics that conform closely to the skin and maintain stable contact and skin comfort during movement. The system also reduces “motion artifacts,” signal disruptions caused when conventional electrodes shift during motion or exercise. In testing, the engineers’ AdapSkin technology improved gesture recognition accuracy in older adults from roughly 60 percent to more than 97 percent.
Over the past several years, Jinxing Li, PhD, assistant professor, College of Engineering and MSU’s Institute for Quantitative Health Science and Engineering, has been developing wearable systems designed to better interface with the human body across a wide range of skin conditions and ages.
“Aging skin changes signal quality,” Li said. “We’ve shown that soft electronics like AdapSkin perform significantly better on older adults’ skin than current commercial electrodes.”
Unlike conventional wearable systems that record signals from only a few points on the skin, AdapSkin uses dense arrays of electrodes to create a more detailed map of muscle activity. Those high-resolution recordings allow researchers to more precisely distinguish between subtle movements, including individual finger motions.
The technology records surface electromyography, or sEMG, signals, which are electrical signals generated when muscles contract and relax. Because those signals reflect instructions sent from the brain to the muscles, they can be used as a noninvasive bridge between the human body and machines.
“With better data, we can better understand the brain’s intended motion,” Li said. “That directly improves the precision and personalization of wearable technology.”
Using the same AI systems and hardware, the higher-quality signals generated by AdapSkin enabled more accurate real-time gesture recognition and robotic control.
That capability is especially important for prosthetics and rehabilitation, according to the researchers. Even after limb loss, the brain continues sending signals to the remaining muscles in the forearm. AdapSkin is sensitive enough to detect those faint electrical patterns, allowing users to control prosthetic devices more naturally by intending a movement.
The sensors remained stable during long-term wear and movement, an important step for real-world rehabilitation and monitoring applications.
As populations age, Li said designing technology that works reliably across different bodies and skin conditions will become increasingly important for healthcare, rehabilitation and future human-machine interfaces.
Editor’s note: This story was adapted from materials provided by Michigan State University.
To watch a video of the sensors, visit the Michigan State University College of Engineering.
