Written By Kevin Kerfoot / Reviewed By Ray Spotts
Researchers have developed a prototype device that electronically replicates the way human skin senses pain. This electronic artificial skin opens the way to better prosthetics, smarter robotics and non-invasive alternatives to skin grafts. The research was published in Advanced Intelligent Systems.
Developed by a team at RMIT University in Melbourne, Australia, the device mimics the body's near-instant feedback response and can react to painful sensations with the same lighting speed that nerve signals travel to the brain. The pain-sensing prototype is a significant advance towards next-generation biomedical technologies and intelligent robotics.
"No electronic technologies have been able to realistically mimic that very human feeling of pain until now,” says lead researcher Professor Madhu Bhaskaran, co-leader of the Functional Materials and Microsystems group at RMIT. "It's a critical step forward in the future development of the sophisticated feedback systems that we need to deliver truly smart prosthetics and intelligent robotics."
Three functional electronic skin prototypes
The researchers also developed devices using stretchable electronics that can sense and respond to changes in temperature and pressure. The three functional prototypes were designed to deliver key features of the skin's sensing capability in electronic form.
The three technologies previously pioneered and patented by the team are stretchable electronics: combining oxide materials with biocompatible silicon to deliver transparent, unbreakable and wearable electronics as thin as a sticker; temperature-reactive coatings: self-modifying coatings 1,000 times thinner than a human hair based on a material that transforms in response to heat; and brain-mimicking memory: electronic memory cells that imitate the way the brain uses long-term memory to recall and retain previous information.
The pressure sensor prototype combines stretchable electronics and long-term memory cells. The heat sensor brings together temperature-reactive coatings and memory, while the pain sensor integrates all three technologies.
With further development, the stretchable artificial skin could also be a future option for non-invasive skin grafts, where the traditional approach is not viable or not working. "We need further development to integrate this technology into biomedical applications but the fundamentals - biocompatibility, skin-like stretchability - are already there," Bhaskaran said.
"We've essentially created the first electronic somatosensors - replicating the key features of the body's complex system of neurons, neural pathways and receptors that drive our perception of sensory stimuli," added Ataur Rahman.
"While some existing technologies have used electrical signals to mimic different levels of pain, these new devices can react to real mechanical pressure, temperature and pain, and deliver the right electronic response. It means our artificial skin knows the difference between gently touching a pin with your finger or accidentally stabbing yourself with it - a critical distinction that has never been achieved before electronically."
Can Deep-Learning E-Skin Decode Complex Human Motion?
Researchers with the Seoul National University and the KAIST School of Computing have developed a deep-learning powered single-strained electronic skin sensor that can capture human motion from a distance.
The single strain sensor placed on the wrist decodes complex five-finger motions in real time with a virtual 3D hand that mirrors the original motions. The deep neural network boosted by rapid situation learning (RSL) ensures stable operation regardless of its position on the surface of the skin and collects data from arbitrary parts on the wrist and automatically trains the model in a real-time demonstration.
To enhance the sensitivity of the sensor, researchers used laser-induced nanoscale cracking. Unlike conventional wafer-based fabrication, this laser fabrication provides a new sensing paradigm for motion tracking. This sensory system can track the motion of the entire body with a small sensory network and facilitate the indirect remote measurement of human motions, which is applicable for wearable VR/AR systems.
The researchers focused on two tasks while developing the sensor. They analyzed the sensor signal patterns into a latent space encapsulating temporal sensor behavior and then they mapped the latent vectors to finger motion metric spaces.
"Our system is expandable to other body parts,” says Professor Sungho Jo from the School of Computing. “We already confirmed that the sensor is also capable of extracting gait motions from a pelvis. This technology is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics."
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