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Mayank Goel


Publications by Mayank Goel (bibliography)

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Larson, Eric C., Goel, Mayank, Boriello, Gaetano, Heltshe, Sonya, Rosenfeld, Margaret and Patel, Shwetak N. (2012): SpiroSmart: using a microphone to measure lung function on a mobile phone. In: Proceedings of the 2012 International Conference on Uniquitous Computing 2012. pp. 280-289. http://dx.doi.org/10.1145/2370216.2370261

Home spirometry is gaining acceptance in the medical community because of its ability to detect pulmonary exacerbations and improve outcomes of chronic lung ailments. However, cost and usability are significant barriers to its widespread adoption. To this end, we present SpiroSmart, a low-cost mobile phone application that performs spirometry sensing using the built-in microphone. We evaluate SpiroSmart on 52 subjects, showing that the mean error when compared to a clinical spirometer is 5.1% for common measures of lung function. Finally, we show that pulmonologists can use SpiroSmart to diagnose varying degrees of obstructive lung ailments.

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

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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. http://dx.doi.org/10.1145/2380116.2380184

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

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