Calendar

May 22

Knowing (by) Designing 2013

May 27

13th Intl. Conf. on New Interfaces for Musical Expression

May 28

Graphics Interface 2013

May 20

“ The moment clients realize that revisions are not an all-you-can-eat buffet, suddenly they realize they are not hungry. ”

-- Lester Beall

Share this quote on...
Get more quotes via RSS or by joining our Usability Quote of the Day Facebook Page

Featured chapter

Read the fascinating history of Wearable Computing, told by its father, Steve Mann

Read Steve's chapter !

Help us help you!

Spread the word:
Donate
Other ways to help

Jeremy Scott

Add description

← Button will not work: You have not enabled Javascript. How do I enable it?

Rename / change spelling

Add publication

← Button will not work: You have not enabled Javascript. How do I enable it?

Publications by Jeremy Scott (bibliography)

2010

Edit | Del

Scott, Jeremy, Dearman, David, Yatani, Koji and Truong, Khai N. (2010): Sensing foot gestures from the pocket. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 199-208.

“Visually demanding interfaces on a mobile phone can diminish the user experience by monopolizing the user's attention when they are focusing on another task and impede accessibility for visually impaired users. Because mobile devices are often located in pockets when users are mobile, explicit foot movements can be defined as eyes-and-hands-free input gestures for interacting with the device. In this work, we study the human capability associated with performing foot-based interactions which involve lifting and rotation of the foot when pivoting on the toe and heel. Building upon these results, we then developed a system to learn and recognize foot gestures using a single commodity mobile phone placed in the user's pocket or in a holster on their hip. Our system uses acceleration data recorded by a built-in accelerometer on the mobile device and a machine learning approach to recognizing gestures. Through a lab study, we demonstrate that our system can classify ten different foot gestures at approximately 86% accuracy.”

Add publication