The stage has been set. There is a new type of material design in the works. It will put the hard, inflexible material to shame. A highly sensitive soft sensor, it leverages textiles in its construction, and when integrated with fabric it becomes a smart robotic apparel.
All About Leveraging Textiles
Tackling the hard, inflexible material properties usually used by popular wearables, the research team at the Wyss Institute for Biologically Inspired Engineering and the John A. Paulson School of Engineering and Applied Sciences (SEAS) at Harvard University might have solved the problem with their highly sensitive soft capacitive sensor.
Made of silicone and fabric that moves and flexes with the human body to unobtrusively and accurately detect movement, the material offers wearer’s natural movements and accuracy of the data collected. On their findings corresponding author Conor Walsh, Ph.D., Core Faculty member at the Wyss Institute and the John L. Loeb Associate Professor of Engineering and Applied Sciences at SEAS said, “We’re really excited about this sensor because, by leveraging textiles in its construction, it is inherently suitable for integration with fabric to make ‘smart’ robotic apparel.” Co-author Ozgur Atalay, Ph.D., Postdoctoral Fellow at the Wyss Institute adds, “Additionally, we have designed a unique batch-manufacturing process that allows us to create custom-shaped sensors that share uniform properties, making it possible to quickly fabricate them for a given application.”
Technology Meets Material Design
According to Lindsay Brownel, who wrote about the research for Wyss Institute for Biologically Inspired Engineering at Harvard’s online publication, the technology consists of a thin sheet of silicone (a poorly conductive material) sandwiched between two layers of silver-plated, conductive fabric (a highly conductive material), forming a capacitive sensor. This type of sensor registers movement by measuring the change in capacitance, or the ability to hold an electrical charge, of the electrical field between the two electrodes. Delving a little deeper, Daniel Vogt, Research Engineer at the Wyss Institute explains, “When we apply strain by pulling on the sensor from the ends, the silicone layer gets thinner and the conductive fabric layers get closer together, which changes the capacitance of the sensor in a way that’s proportional to the amount of strain applied, so we can measure how much the sensor is changing shape.”
The hybrid sensor’s superior performance stems from its novel manufacturing process, in which the fabric is attached to both sides of the silicone core with an additional layer of liquid silicone that is subsequently cured. This method allows the silicone to fill some of the air gaps in the fabric, mechanically locking it to the silicone and increasing the surface area available for distributing strain and storing electrical charge. This silicone-textile hybrid improves sensitivity to movement by capitalizing on the qualities of both materials: the strong, interlocking fabric fibers help limit how much the silicone deforms while stretching, and the silicone helps the fabric return to its original shape after the strain is removed. Finally, thin, flexible wires are permanently attached to the conductive fabric with thermal seam tape, allowing electrical information from the sensor to be transmitted to a circuit without a hard, bulky interface.
Robotics, Solving the Problem
The team evaluated their new sensor design by performing strain experiments in which various measurements are taken as the sensor is stretched by an electromechanical tester. Generally, as an elastic material is pulled, its length increases while its thickness and width decrease, so the total area of the material — and, therefore, its capacitance — stays constant. Surprisingly, the researchers found that the conductive area of their sensor increased as it was stretched, resulting in greater-than-expected capacitance. “Silicone-based capacitive sensors have limited sensitivity based on the nature of material. Embedding the silicone in conductive fabric, however, created a matrix that prevented the silicone from shrinking as much width-wise, which improved sensitivity above that of the bare silicone we tested,” says lead author Asli Atalay, Postdoctoral Fellow at the Wyss Institute.
The hybrid sensor detected increases in capacitance within 30 milliseconds of strain application and physical changes of less than half a millimeter, confirming that it is capable of capturing movement on the scale of the human body. To test that ability in a real-world scenario, the team integrated a set of them into a glove to measure fine-motor hand and finger movements in real time. The sensors were successfully able to detect capacitance changes on individual fingers as they moved, indicating their relative positions over time. “Our sensor’s greater sensitivity means it has the ability to distinguish smaller movements, like slightly moving one finger side-to-side rather than simply whether the whole hand is open or clenched in a fist,” explains co-author Vanessa Sanchez, a Graduate Student in the Biodesign Lab at SEAS.
“This work represents our growing interest in leveraging textile technology in robotic systems”
While this study is a preliminary proof-of-concept, the team is excited about the many future directions in which this technology could develop. “This work represents our growing interest in leveraging textile technology in robotic systems, and we see promising applications for motion capture ‘in the wild,’ such as athletic clothing that tracks physical performance or soft clinical devices to monitor patients in their homes. In addition, when combined with fabric-based soft actuators, these sensors will enable new robotic systems that truly mimic apparel,” says Walsh.
Although this new technology could open up an entirely new approach for wearables, the study is still only a preliminary proof-of-concept. That being said the team is not deterred, they are instead excited about the future directions and developments that this technology could deliver.