Learning Situation Models in a Smart Home

By Oliver Brdiczka, James L. Crowley, P. Reignier. 2009

186 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Narayanan Chatapuram Krishnan
2. Diane Joyce Cook
3. Jadwiga Indulska
4. Karen Henricksen
5. G. Sukhatme
6. Maja J. Mataric
7. C. Nugent
8. Anand V. Panangadan

In Electrical and Electronic Engineering

1. Paolo Rocca
2. Giacomo Oliveri
3. Narayanan Chatapuram Krishnan
4. Andrea Massa
5. Diane Joyce Cook
6. G. Sukhatme
7. Maja J. Mataric
8. Billy Pik Lik Lau

In Transportation

1. Diane Joyce Cook
2. Narayanan Chatapuram Krishnan
3. Chau Yuen
4. Billy Pik Lik Lau
5. G. Sukhatme
6. Maja J. Mataric
7. Anand V. Panangadan
8. Jesse Hoey

Citations in the corpus

• A survey of data fusion in smart city applications (2019, background): “In addition, the modeling of human behavior in a smart home [149] in order to generate learning situation models have proven the efficiency of context-aware services.”
• A Survey on Ambient Intelligence in Health Care (2013, unknown): “Other modalities that have been researched for activity recognition include video cameras [58], [59], [60], [61], microphones [62], [63], and GPS locators [64], [65].”
• A Review of Smart Homes—Past, Present, and Future (2012, background): “can also be applied for activity recognition [24].”
• A survey of context modelling and reasoning techniques (2010, background): “Most context definitions mention space as a vital factor: e.g., Schilit, Adams andWant define three important aspects of context as ‘‘Where you are, who you are with and what resources are nearby’’ [14].”
• Tracking and Modeling of Human Activity Using Laser Rangefinders (2010, methodology): “[6] where a model from cognitive theory (Situation model)”
• Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation (2013, background): “…models, such as Bayes classifiers [54], [61], Markov models [62], [63], artificial neural networks [64], [65], and support vector machines (SVMs) [66], [67], thanks to their ability to create context models from measured data and adapt according to the changing behavior and needs, and their…”
• Activity Recognition on Streaming Sensor Data (2012, methodology): “Researchers have also used data from video cameras monitoring and recognizing different activities [24][25].”
• Sensor-Based Activity Recognition (2012, methodology): “[91] where a model of situations is learned automatically from data by first learning roles of various entities using SVMs and labeled training data, then using unsupervised clustering to”