Real-time health monitoring and sensing abilities of robots require soft electronics, but a challenge of using such materials lies in their reliability. 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 new sensor material that has significantly less hysteresis. This ability enables more accurate wearable health technology and robotic sensing.

When soft materials are used as compressive sensors, they usually face severe hysteresis issues. The soft sensor’s material properties can change between repeated touches, which affects the reliability of the data. This makes it challenging 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 almost hysteresis-free performance. They developed a process to crack metal thin films into desirable ringshaped patterns on a flexible material called polydimethylsiloxane (PDMS).

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

“With our unique design, we were able to achieve significantly improved accuracy and reliability. 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,” said Assistant Professor Benjamin Tee.

The next step for the NUS team is to further improve the conformability of their 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. This TRACE sensor is a step forward towards that reality because the data it can capture for pulse velocities is more accurate and can also be equipped with machine learning algorithms to predict surface textures more accurately,” explained Tee.

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

For more information, contact Carolyn Fong at This email address is being protected from spambots. You need JavaScript enabled to view it..