Microsoft has released a new video demonstrating a motion sensing mechanical keyboard.
The keyboard blends traditional typing with motion based gestures, letting your hands remain in the home position on the keyboard. Hover gestures above the keyboard let you switch between applications. Navigating documents is possible using core swipes and pinch-to-zoom.
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We present a new type of augmented mechanical keyboard, sensing rich and expressive motion gestures performed both on and directly above the device. A low-resolution matrix of infrared (IR) proximity sensors is interspersed with the keys of a regular mechanical keyboard. This results in coarse but high frame-rate motion data.
We extend a machine learning algorithm, traditionally used for static classification only, to robustly support dynamic, temporal gestures. We propose the use of motion signatures a technique that utilizes pairs of motion history images and a random forest classifier to robustly recognize a large set of motion gestures. Our technique achieves a mean per-frame classification accuracy of 75:6% in leave–one–subject–out and 89:9% in half-test/half-training cross-validation. We detail hardware and gesture recognition algorithm, provide accuracy results, and demonstrate a large set of gestures designed to be performed with the device. We conclude with qualitative feedback from users, discussion of limitations and areas for future work.
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Take a look at the video below...
Read More [via RedmondPie]
The keyboard blends traditional typing with motion based gestures, letting your hands remain in the home position on the keyboard. Hover gestures above the keyboard let you switch between applications. Navigating documents is possible using core swipes and pinch-to-zoom.
---
We present a new type of augmented mechanical keyboard, sensing rich and expressive motion gestures performed both on and directly above the device. A low-resolution matrix of infrared (IR) proximity sensors is interspersed with the keys of a regular mechanical keyboard. This results in coarse but high frame-rate motion data.
We extend a machine learning algorithm, traditionally used for static classification only, to robustly support dynamic, temporal gestures. We propose the use of motion signatures a technique that utilizes pairs of motion history images and a random forest classifier to robustly recognize a large set of motion gestures. Our technique achieves a mean per-frame classification accuracy of 75:6% in leave–one–subject–out and 89:9% in half-test/half-training cross-validation. We detail hardware and gesture recognition algorithm, provide accuracy results, and demonstrate a large set of gestures designed to be performed with the device. We conclude with qualitative feedback from users, discussion of limitations and areas for future work.
---
Take a look at the video below...
Read More [via RedmondPie]