1 TAPSENSE ENHANCING FINGER INTERACTION ON TOUCH SURFACES In proceedings of 24 th ACM UIST symposium, 2011, Santa Barbara, CA

AUTHORS 2 Recognized by MIT’s Technology review as one of the worlds top 35 innovators under 35 Recipient of Google’s Ph.D Fellowship in 2012 Current Carnegie Mellon Ph.D student Recipient of Microsoft’s Ph.D fellowship in 2012 Current Carnegie Mellon Ph.D student Expert Juggler Professor in the HCI institute of Carnegie Mellon Univ. Inducted into the CHI academy in 2006 Founding director of the Ph.D program in HCI at Carnegie Mellon Univ. CHRIS HARRISONJULIA SCHWARZSCOTT E. HUDSON

BACKGROUND 3 The use of touch interfaces has significantly increased, sophistication of input has not Tablet PC touchscreens Mobile touchscreens Fingers are very sophisticated, can be used in a more efficient way than the x/y coordination method currently used to measure finger input

TAPSENSE 4 Gives the touchscreen the ability to identify the type of object used as input Can differentiate between a nail, tip, knuckle, and pad Tapsense relies on the fact that different parts of the finger produce different acoustic signatures Many features based on what has been done with skinput technology Oh no! The dreaded Fat Fingers May solve two current issues with touch interactions: Finger Overloading Breaking out functionality

5 Ipod Touch with acoustic sensor attached Acoustic sensor connected to computer to record Discussed the possibility of mobile phones to use built in microphones to implement tapsense MOBILE PROOF OF CONCEPT

6 TABLE PROOF OF CONCEPT

7 SAMPLE APPLICATION

EVALUATION 8 18 Participants involved 9 Male, 9 Female 9 participants for the prototype mobile device 4 female, 5 male Tested input set of Pad, Tip, Knuckle, Nail, and stylus 9 participants for the multitouch table 5 female, 4 males Tested all finger inputs as well 7 input tools 10 taps to different screen locations, done 4 times

RESULTS 9 Mobile Accuracy: 88.3% with all input types Tip was worst performing Finger and pad have accuracy rating of 99.4% Tablet Accuracy 86.3% with all input types Tip also worst performing Finger and pen combo – accuracy of 99.7%

10 Any other questions? Is Tapsense limited to mobile devices, or could it be integrated with any other technologies mentioned in the paper? How do you imagine TapSense helping you? Is this the end of buttons/keypads? Will we soon see technology like TapSense in our mobile devices? QUESTIONS THANKS!