Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Activity sensing in the wild: a field trial of ubifit garden
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Flowers or a robot army?: encouraging awareness & activity with personal, mobile displays
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
An 'object-use fingerprint': the use of electronic sensors for human identification
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Toolkit to support intelligibility in context-aware applications
Proceedings of the 12th ACM international conference on Ubiquitous computing
Implicit authentication through learning user behavior
ISC'10 Proceedings of the 13th international conference on Information security
Accelerometer-based on-body sensor localization for health and medical monitoring applications
Pervasive and Mobile Computing
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
ACCessory: password inference using accelerometers on smartphones
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
Progressive authentication: deciding when to authenticate on mobile phones
Security'12 Proceedings of the 21st USENIX conference on Security symposium
Accelerometers data interoperability: easing interactive applications development
Proceedings of the 18th Brazilian symposium on Multimedia and the web
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We show that accelerometers embedded in a television remote control can be used to distinguish household members based on the unique way each person wields the remote. This personalization capability can be applied to enhance digital video recorders with show recommendations per family-member instead of per device or as an enabling technology for targeted advertising. Based on five 1-3 week data sets collected from real homes, using 372 features including key press codes, key press timing, and 3-axis acceleration parameters including dominant frequency, energy, mean, and variance, we show household member identification accuracy of 70-92% with a Max-Margin Markov Network (M3N) classifier.