Highly Sensitive Nanocapacitive Sensor for A Human-Machine Interface

J-Hn Chung
University of Washington,
United States

Keywords: capacitive sensor, carbon nanotubes, human-machine interface, noncontact detection

Summary:

We present a highly sensitive nanocapactive sensor for delicate monitoring of human behavior. The sensor is made of a carbon nanotube-paper composite (CPC) that is inexpensive and highly sensitive for non-contact detection of skin movement. Cellulose fibers are infused with multi-walled carbon nanotubes (MWCNT) to create a highly conductive CPC. The CPC is stretched to induce a crack due to the fracture of the tensional directional fibers. The cracked region acts as a pair of parallel electrodes, creating a highly sensitive capacitive sensor made of MWCNT tips. The CPC sensor enables a drastic reduction in the form factor in comparison to traditional capacitive sensors, with unparalleled sensitivity. The sensors are integrated with a capacitance-digital-converter chip, which negates bulky analog circuitry and reduces parasitic capacitance. The sensor circuit provides a straightforward digital readout. This proprietary sensor technology allowed the integration of flexible sensors onto a human body for behavior monitoring. The development of the CPC sensor directly impacts the field of human-machine interface, wearable sensors, and smart devices. For potential applications, we will demonstrate both a hand gesture recognition system and a wearable eye tracker. A hand gesture recognition system can replace rudimentary push, displacement, and rotary buttons in personal devices, transportation, healthcare, and vehicular control. A controller that recognizes the movement of human fingers or hands will translate a hand gesture into an assigned order. An array of CPC sensors are integrated onto a prototype with a small form factor. This technology is being further developed with a unique gesture recognition algorithm to accurately map the movement of the human hand for a human-machine interface. A wearable eye tracking device is demonstrated to monitor eye movement in real-time. CPC sensors are integrated onto eyeglasses to track eyeball movement and monitor eye flickering. Current eye-tracking systems are video camera-based detection that includes eyeglasses mounted with cameras and desktop monitors. The camera-based eye trackers present challenges in terms of usability, data quality, and cost. The high computational cost and the high power demand are additional burdens for a wearable platform with a small form factor. For truly wearable real-time eye-tracking, CPC sensors are mounted on eyeglasses to detect up/down and left/right movements of an eyeball. The sensors monitor eye movement using a microprocessor via capacitance-to-digital chips. The wearable eye tracker will impact brain-related health care, cognitive monitoring, automotive, consumer electronics, and entertainment including VR/AR industry.