The robot’s ability to track and feel real-time health requires soft electronics, but the challenge of using these materials lies in their reliability. Unlike hard devices, elasticity and flexibility make their performance less repeatable. The variation in reliability is called latency.
Guided by the theory of contact mechanics, a team of researchers from the National University of Singapore (NUS) came up with a new type of sensor material with significantly less latency. This capability allows health technologies to be more accurately wearable and robotic sensors.
The team, led by Assistant Professor Benjamin Tee from the NUS Institute of Medical Technology & Innovation, published their results in the prestigious journal Proceedings of the National Academy of Sciences on September 28, 2020.
High sensitivity, low latency pressure sensor
When using soft materials as compression sensors, they often experience severe lag problems. The material properties of the soft sensor can change between repeated touches, which affects the reliability of the data. This makes it difficult to read accurately all the time, limiting possible sensor applications.
The NUS team̵7;s breakthrough was the invention of a highly sensitive material, but with virtually no lag performance. They developed a process to break metal thin films into desired ring patterns on a flexible material called polydimethylsiloxane (PDMS).
The team integrated this metal / PDMS membrane with electrodes and substrates for the voltage sensor and characterizes its performance. They conducted numerous mechanical tests and verified that their design improvements improved sensor performance. Their invention, dubbed E-Skin Leather has a tactile resistance, or TRACE, that is five times better than conventional soft materials.
“With our unique design, we have been able to achieve significantly improved accuracy and reliability. A capable TRACE sensor can be used in robots to sense surface textures or in wearable health technology devices, for example, to measure blood flow in surface arteries for using health monitoring “ Professor Tee, who is also from the NUS Department of Engineering and Materials Science.
The NUS team’s next step is to further improve the suitability of their materials for various wearable applications and develop artificial intelligence (AI) applications based on sensors.
“Our long-term goal is to predict cardiovascular health in the form of a smart little patch placed on the human skin. This TRACE sensor is a step up to that reality as the data it can collect for more accurate pulse velocities and can also be equipped with machine learning algorithms for accurate surface texture prediction. more, “ Professor Tee explained.
Other applications the NUS team is aiming to develop include use in prosthetics, where reliable skin interfaces allow smarter responses.
Refer to Magazines
- Haicheng Yao, Low-latency Soft Touch Electronic Leather for wearable and reliable machine learning, DOI: 10.1073 / pnas.