Researchers at the National University of Singapore (NUS) have developed a flexible and reliable sensor for wearable health devices and robotic perception.

Real-time health monitoring and sensing abilities of robots require soft electronics, but a problem with using such materials is their reliability. Unlike rigid devices, being elastic and pliable makes their performance less repeatable. The variation in reliability is known as hysteresis.

Guided by the theory of contact mechanics, a team of researchers from NUS came up with a sensor material that has significantly less hysteresis. This ability enables more accurate wearable health technology and robotic sensing.

The research team, led by assistant professor Benjamin Tee from the Institute for Health Innovation & Technology at NUS, published the results in the Proceedings of the National Academy of Sciences.

When soft materials are used as compressive sensors, they usually face severe hysteresis issues. The soft sensor’s material properties can change in between repeated touches, which affects the reliability of the data. This makes it difficult to get accurate readouts every time, limiting the sensors’ possible applications.

The NUS team’s breakthrough is the invention of a material that has high sensitivity, but with an almost hysteresis-free performance. They developed a process to crack metal thin films into desirable ring-shaped patterns on a flexible material called polydimethylsiloxane (PDMS).

The team integrated this metal-PDMS film with electrodes and substrates for a piezoresistive sensor and characterised its performance. They conducted repeated mechanical testing and verified that their design improved sensor performance. Their invention, named Tactile Resistive Annularly Cracked E-skin, or Trace, is said to be five times better than conventional soft materials.

“With our unique design, we were able to achieve significantly improved accuracy and reliability,” said Tee. “The Trace sensor could potentially be used in robotics to perceive surface texture or in wearable health technology devices, for example, to measure blood flow in superficial arteries for health monitoring applications.”

The next step for the team is to improve the conformability of the material for different wearable applications and to develop artificial intelligence (AI) applications based on the sensors.

“Our long-term goal is to predict cardiovascular health in the form of a tiny smart patch that is placed on human skin,” said Tee. “This Trace sensor is a step forward towards that reality because the data it can capture for pulse velocities are more accurate, and can also be equipped with machine-learning algorithms to predict surface textures more accurately.”

Other applications the NUS team aims to develop include uses in prosthetics, where having a reliable skin interface allows for a more intelligent response.