Stretchable and Self-Healing Sensor Could Provide Precision Human-Motion Detection

The researchers' new strain sensor can monitor a variety of human motions in real-time.

Cabe Atwell
5 years agoWearables / Sensors
The sensor was designed using an ionic and conductive polyacrylamide (PAAm) hydrogel, which can repair itself when torn or damaged. (📷 Hang et al.)

Researchers from Fudan University, Tongji University, and the Chinese Academy of Sciences have developed a highly-stretchable and self-healing sensor that could be used for precision motion tracking for human and robotic movements. The sensor is similar to other strain-based sensors used to convert force, pressure, tension, and weight into a change in electrical resistance that can be accurately measured. These types of sensors are already packed into a myriad of different devices to detect motion, including health-monitoring devices and HMIs (human-machine interfaces).

In their recently released paper, the researchers outline how they developed the precision sensor, which is fabricated using an ionic and conductive polyacrylamide (PAAm) hydrogel. It's that gel that allows the material to self-heal when torn or damaged.

"The ionic conductive PAAm hydrogel shows an excellent self-healing property with fast electrical self-healing speed (within 1.8 seconds) and high self-healing efficiency (99%)," the team stated in the paper. The PAAm hydrogel-based strain sensor exhibits excellent performance with large stretchability (>900%), high sensitivity (with maximum gage factor of 6.44), fast response time (~150ms) and good cycling durability (>3,000 cycles)."

As mentioned earlier, the strain sensor was designed to monitor human motions in real-time, and when integrated with integrated circuits, can transfer data to smart devices (phones, tablets, laptops, etc.) via a Bluetooth connection. The researchers demonstrated their new technology by creating a wearable PCB outfitted with the sensor and controlled by an Arduino Nano microcontroller. The device was strapped to a volunteer's bicep and successfully monitored motion while working out, which was recorded to a smartphone to analyze the data with a Kivy-based app.

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