Liquid Metal Sensors Recreate a Sense of Touch
To recreate a sense of ‘touch’, researchers have incorporated stretchable tactile sensors using liquid metal on the fingertips of a prosthetic hand.
When manipulating an object, humans are heavily reliant on sensation in their fingertips, each of which has over 3000 pressure-sensitive touch receptors. While there are many high-tech, dexterous prosthetics available today, they all lack the sensation of ‘touch‘, resulting in objects inadvertently being dropped or crushed by a prosthetic hand.
To make a prosthetic hand interface that feels more natural and intuitive, researchers from Florida Atlantic University’s College of Engineering and Computer Science and collaborators incorporated stretchable tactile sensors using liquid metal on a prosthetic hand’s fingertips. Encapsulated within silicone-based elastomers, this technology provides key advantages over traditional sensors, including high conductivity, compliance, flexibility and stretchability.
For the study, published in the journal Sensors, researchers used individual fingertips on the prosthesis to distinguish between different speeds of a sliding motion along different textured surfaces. The four different textures had one variation: the distance between the ridges. To detect the textures and speeds, researchers trained four machine learning algorithms. For each of the ten surfaces, 20 trials were performed to test the ability of the machine learning algorithms to distinguish between the different textured surfaces.
Results showed that integrating tactile information from the fingertip sensors simultaneously distinguished between complex, multi-textured surfaces – demonstrating a new form of hierarchical intelligence. The algorithms could accurately distinguish between the fingertip speeds. This new technology could improve prosthetic hand control and provide haptic feedback for amputees to restore a sense of touch.
“Significant research has been done on tactile sensors for artificial hands, but there is still a need for advances in lightweight, low-cost, robust multimodal tactile sensors,” said senior author Erik Engeberg, PhD, an associate professor in the Department of Ocean and Mechanical Engineering. “The tactile information from all the individual fingertips in our study provided the foundation for a higher hand-level of perception enabling the distinction between ten complex, multi-textured surfaces that would not have been possible using purely local information from an individual fingertip. We believe that these tactile details could be useful in the future to afford a more realistic experience for prosthetic hand users through an advanced haptic display, which could enrich the amputee-prosthesis interface and prevent amputees from abandoning their prosthetic hand.”
Researchers compared four different machine learning algorithms for their successful classification capabilities. The time-frequency features of the liquid metal sensors were extracted to train and test the machine learning algorithms. Of these, a neural network algorithm generally performed the best at the speed and texture detection with a single finger and had a 99.2 percent accuracy to distinguish between ten different multi-textured surfaces using four liquid metal sensors from four fingers simultaneously.
“The loss of an upper limb can be a daunting challenge for an individual who is trying to seamlessly engage in regular activities,” said Stella Batalama, Ph.D., dean, College of Engineering and Computer Science. “Although advances in prosthetic limbs have been beneficial and allow amputees to better perform their daily duties, they do not provide them with sensory information such as touch. They also don’t enable them to control the prosthetic limb naturally with their minds. With this latest technology from our research team, we are one step closer to providing people all over the world with a more natural prosthetic device that can ‘feel’ and respond to its environment.”
Source: Florida Atlantic University
Journal information: Abd, M.A., et al. (2021) Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition. Sensors. doi.org/10.3390/s21134324.