Touch me Gently Recreates Natural Touch Sensation Using Shape-Memory Alloy Matrix

This system features stretchable plasters embedded with shape-memory alloys to simulate the perception of touch.

Cabe Atwell
6 months agoWearables / Sensors

Engineers from the Technical University of Applied Sciences Lübeck and the University of Auckland have come up with a novel platform that recreates a natural touch sensation using a shape-memory alloy matrix, which is accomplished by applying shear-forces onto the skin. While most haptic feedback systems employ servos, solenoids, and vibration motors to produce a feeling sensation, they are often large and cumbersome to use. With the introduction of lightweight, soft, and flexible haptic actuators created with shape-memory alloys, the feeling of natural touch becomes more prominent.

The researchers developed their Touch me Gently device around those shape-memory alloys (SMAs), specifically SMA-based plasters that are arranged in a matrix. The lightweight, stretchable 3 x 3cm plasters (15 in all) were designed with multiple layers, including a thermal dissipation layer, SMA wire/terminal layer, a stretchable adhesive textile, and a thermal/electrical insulation layer.

The plasters are connected to a custom PCB outfitted with a dsPIC33CH64MP202T microcontroller, which drives eight actuator bits independently, while a pair of ULN2003F12FN-7 Darlington arrays handle four channels for each matrix. It’s also equipped with a BTS3080EJXUMA1 power switch, an FT230XQ-R FTDI USB interface for programming, and a 3.7V Lithium battery for power.

The plasters deform when subjected to lower temperatures and revert to their original shape when heated or when an electric current is supplied. The engineers were able to program the Touch me Gently device to produce a series of touching sensations, such as grabbing an arm or wrist, tapping feelings up and down an arm, stroking, and outlining a circle on an arm or wrist.

The team tested their natural touch system on volunteers who were able to distinguish each touch pattern with a 94.75% accuracy, and have demonstrated how the device could be used for AR and VR applications.

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