Paper
8 March 2014 Enabling large scale capacitive sensing for dielectric elastomers
Author Affiliations +
Abstract
Hand motion is one of our most expressive abilities. By measuring our interactions with everyday objects, we can create smarter artificial intelligence that can learn and adapt from our behaviours and patterns. One way to achieve this is to apply wearable dielectric elastomer strain sensors directly onto the hand. Applications such as this require fast, efficient and scalable sensing electronics. Most capacitive sensing methods use an analogue sensing signal and a backend processor to calculate capacitance. This not only reduces scalability and speed of feedback but also increases the complexity of the sensing circuitry. A capacitive sensing method that uses a DC sensing signal and continuous tracking of charge is presented. The method is simple and efficient, allowing large numbers of dielectric elastomer sensors to be measured simulatenously.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniel Xu, Thomas G. McKay, Silvain Michel, and Iain A. Anderson "Enabling large scale capacitive sensing for dielectric elastomers", Proc. SPIE 9056, Electroactive Polymer Actuators and Devices (EAPAD) 2014, 90561A (8 March 2014); https://doi.org/10.1117/12.2044356
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CITATIONS
Cited by 12 scholarly publications.
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KEYWORDS
Capacitance

Sensors

Dielectrics

Artificial intelligence

Electrodes

Capacitors

Motion measurement

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