Survey of the state of the art in human language technology
Statistical methods for speech recognition
Statistical methods for speech recognition
Digital family portraits: supporting peace of mind for extended family members
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
EasyLiving: Technologies for Intelligent Environments
HUC '00 Proceedings of the 2nd international symposium on Handheld and Ubiquitous Computing
Location Aware Resource Management in Smart Homes
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Lessons learned using ubiquitous sensors for data collection in real homes
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Buzz: measuring and visualizing conference crowds
ACM SIGGRAPH 2007 emerging technologies
Proceedings of the 9th international conference on Multimodal interfaces
The MERL motion detector dataset
Proceedings of the 2007 workshop on Massive datasets
Recognizing daily activities with RFID-based sensors
Proceedings of the 11th international conference on Ubiquitous computing
Activity recognition in smart homes: from specification to representation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Recognition of hand movements using wearable accelerometers
Journal of Ambient Intelligence and Smart Environments
Socialmotion: measuring the hidden social life of a building
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
UDS: sustaining quality of context using uninterruptible data supply system
QuaCon'09 Proceedings of the 1st international conference on Quality of context
Tracking your steps on the track: body sensor recordings of a controlled walking experiment
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
A language-based approach to indexing heterogeneous multimedia lifelog
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Recognition of hand movements using wearable accelerometers
Journal of Ambient Intelligence and Smart Environments
Modeling and simulation for user assistance in smart environments
Proceedings of the Winter Simulation Conference
Robotics and Autonomous Systems
An unsupervised recommender system for smart homes
Journal of Ambient Intelligence and Smart Environments - Ambient and Smart Component Technologies for Human Centric Computing
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Pervasive sensors in the home have a variety of applications including energy minimization, activity monitoring for elders, and tutors for household tasks such as cooking. Many of the common sensors today are binary, e.g. IR motion sensors, door close sensors, and floor pressure pads. Predicting user behavior is one of the key enablers for applications. While we consider smart home data here, the general problem is one of predicting discrete human actions. Drawing on Activity Theory, the language as action principle, and speech understanding research, we argue that smoothed n-grams are very appropriate for this task. We built such a model and applied it to data gathered from 3 smart home installations. The data showed a classic Zipf or power-law distribution, similar to speech and language. We found that the predictive accuracy of the n-gram model ranges from 51% to 39%, which is significantly above the baseline for the deployments of 16, 76 and 70 sensors. While we cannot directly compare this result with other work (lack of shared data), by examination of high entropy zones in the datasets (e.g. the kitchen triangle) we argue that accuracies around 50% are best possible for this task.