Keyboard or controller? New fabric-based sensor lets you control video games from your sleeve

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New research from North Carolina State University combines three-dimensional embroidery technology with machine learning to create a fabric-based sensor that can control electronic devices through touch.

As interest in the field of wearable electronics grows and new features are added to clothing, embroidery-based sensors or “buttons” that can control those features become increasingly important. Sensors are embedded in the fabric of the garment and are activated to provide complete touch control of electronic devices such as mobile apps.

This device consists of two parts. The embroidered pressure sensor itself and a microchip that processes and distributes the data collected by the sensor. The sensor is triboelectric. This means that the charge generated by the friction between multiple layers is used to power the sensor itself. It is made from a thread of two triboelectric materials, one positively charged and one negatively charged, and is incorporated into a conventional textile fabric using an embroidery machine.

Long Yin, corresponding author of the study, said the three-dimensional structure of the sensor is important for its correct use.

“The pressure sensor is triboelectric, so we needed two layers with a gap in between. Embroidery is flat, so that gap was the challenge.

It is a technique for decorating fabrics,” he said. “It's difficult to create three-dimensional structures like this. By using spacers, we were able to control the gap between the two layers, which allowed us to control the output of the sensor.”

Data from the pressure sensor is sent to a microchip, which converts the raw input into specific instructions for the connected device. Machine learning algorithms are key to making this happen smoothly, Yin said. The device not only needs to be able to distinguish between gestures assigned to different functions, but also needs to be able to ignore unintended inputs that may result from the normal movement of the cloth.

“Sometimes the data that the sensor gets is not very accurate, and this can happen for all kinds of reasons,” Yin says. “In some cases, the data may be affected by environmental factors such as temperature or humidity, or the sensor may accidentally touch something. Machine learning can be used to recognize such things. You can train your device.

“Machine learning allows us to recognize different types of inputs, allowing this very small device to perform different tasks.”

The researchers demonstrated this input recognition by developing a simple music-playing mobile app that connected to the sensor via Bluetooth. They designed six functions for the app: play/pause, next song, last song, volume up, volume down, and mute, each controlled by a different gesture on the sensor. The researchers were able to use the device for several other functions, including setting and entering passwords and controlling video games.

Yin said the idea is still in its early stages because existing embroidery techniques cannot easily handle the type of materials used to create the sensors. Still, the new sensor represents another piece of the wearable electronics puzzle in development, and is sure to continue to generate interest in the near future.

reference: Chen Y, Ling Y, Yang Y et al. Clickable embroidered triboelectric sensors for smart fabrics. device. 2024:100355. doi: 10.1016/j.device.2024.100355

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