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Vioguard Self-Sanitizing Keyboard Bathes Itself in Ultraviolet Light to Kill the Germs

January 4th, 2012 Wouter Stomp Critical Care, Medicine

Vioguard has received FDA clearance for a self-sanitizing computer keyboard that uses the germicidal properties of ultraviolet light (UV-C) to automatically clean its surfaces, eliminating many harmful viruses and germs.

The keyboard protracts into a light-tight enclosure which bathes it in germ-killing light. It has a standard USB connection and also includes a multitouch trackpad.

How it works, from the product page:

Vioguard’s product has the look and feel of a standard notebook keyboard and requires no software or special hardware to function.

The Vioguard keyboard is stored in a mechanized enclosure (which doubles as a monitor stand) when not in use. It safely floods the keyboard and track pad with germicidal ultraviolet light. When
a user sits down at the workstation and activates the infrared motion sensor with the wave of a hand, the keyboard automatically extends into working position. When the user is finished, the keyboard automatically retracts back inside the enclosure and activates the germicidal ultraviolet lights.

LED indicators are used to let the user know when the keyboard has been sanitized and is ready for use. The sanitization cycle can be interrupted anytime to ensure the keyboard is available as needed.

A standard cleaning cycle takes around 65-95 seconds. A recent small clinical trial with the keyboard in the intensive care unit and emergency department of a pediatric hospital showed that it was 67% effective in eliminating bacterial contamination as measured by quantitative bacterial cultures, although it did not eliminate the need for regular physical cleaning.

Press release: FDA Clears Vioguard Self-Sanitizing Keyboard for Use in U.S. Hospitals and Medical Clinics…

Product page: Vioguard…

Wouter Stomp

Wouter Stomp is a radiology resident at the radiology department of Leiden University Medical Center, the Netherlands. He is also a PhD candidate with his research focussing on the role of MRI in early rheumatoid arthritis.

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