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Shwetak Patel

 

Publications by Shwetak Patel (bibliography)

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2012
 
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Goel, Mayank, Wobbrock, Jacob and Patel, Shwetak (2012): GripSense: using built-in sensors to detect hand posture and pressure on commodity mobile phones. In: Proceedings of the 2012 ACM Symposium on User Interface Software and Technology 2012. pp. 545-554. Available online

We introduce GripSense, a system that leverages mobile device touchscreens and their built-in inertial sensors and vibration motor to infer hand postures including one- or two-handed interaction, use of thumb or index finger, or use on a table. GripSense also senses the amount of pres-sure a user exerts on the touchscreen despite a lack of direct pressure sensors by inferring from gyroscope readings when the vibration motor is "pulsed." In a controlled study with 10 participants, GripSense accurately differentiated device usage on a table vs. in hand with 99.67% accuracy and when in hand, it inferred hand postures with 84.26% accuracy. In addition, GripSense distinguished three levels of pressure with 95.1% accuracy. A usability analysis of GripSense was conducted in three custom applications and showed that pressure input and hand-posture sensing can be useful in a number of scenarios.

© All rights reserved Goel et al. and/or ACM Press

2011
 
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Larson, Eric, Cohn, Gabe, Gupta, Sidhant, Ren, Xiaofeng, Harrison, Beverly, Fox, Dieter and Patel, Shwetak (2011): HeatWave: thermal imaging for surface user interaction. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 2565-2574. Available online

We present HeatWave, a system that uses digital thermal imaging cameras to detect, track, and support user interaction on arbitrary surfaces. Thermal sensing has had limited examination in the HCI research community and is generally under-explored outside of law enforcement and energy auditing applications. We examine the role of thermal imaging as a new sensing solution for enhancing user surface interaction. In particular, we demonstrate how thermal imaging in combination with existing computer vision techniques can make segmentation and detection of routine interaction techniques possible in real-time, and can be used to complement or simplify algorithms for traditional RGB and depth cameras. Example interactions include (1) distinguishing hovering above a surface from touch events, (2) shape-based gestures similar to ink strokes, (3) pressure based gestures, and (4) multi-finger gestures. We close by discussing the practicality of thermal sensing for naturalistic user interaction and opportunities for future work.

© All rights reserved Larson et al. and/or their publisher

 
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