SenSay: A Context-Aware Mobile Phone
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Lightweight material detection for placement-aware mobile computing
Proceedings of the 21st annual ACM symposium on User interface software and technology
HandSense: discriminating different ways of grasping and holding a tangible user interface
Proceedings of the 3rd International Conference on Tangible and Embedded Interaction
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Symbolic object localization through active sampling of acceleration and sound signatures
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
On-body device localization for health and medical monitoring applications
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
Getting closer: an empirical investigation of the proximity of user to their smart phones
Proceedings of the 13th international conference on Ubiquitous computing
A rotation based method for detecting on-body positions of mobile devices
Proceedings of the 13th international conference on Ubiquitous computing
Polite ringer II: a ringtone interaction system using sensor fusion
Proceedings of the 13th international conference on Ubiquitous computing
PocketTouch: through-fabric capacitive touch input
Proceedings of the 24th annual ACM symposium on User interface software and technology
Where am i: recognizing on-body positions of wearable sensors
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Stop questioning me!: towards optimizing user involvement during data collection on mobile devices
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
Combination and abstraction of sensors for mobile context-awareness
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Enabling phones to infer whether they are currently in a pocket, purse or on a table facilitates a range of new interactions from placement-dependent notifications setting to preventing "pocket dialing". We collected data from 693 participants to understand where people keep their phone in different contexts and why. Using this data, we identified three placement personas: Single Place Pat, Consistent Casey, and All-over Alex. Based on these results, we collected two weeks of labeled accelerometer data in-situ from 32 participants. We used this data to build models for inferring phone placement, achieving an accuracy of approximately 85% for inferring whether the phone is in an enclosed location and for inferring if the phone is on the user. Finally, we prototyped a capacitive grid and a multispectral sensor and collected data from 15 participants in a laboratory to understand the added value of these sensors.