Sensor Selection in Smart Homes

Abstract There has been a lot of work focussing on activity recognition in smart homes, with the aim of the home being to monitor the activities of the inhabitant and identify deviations from the norm. For a smart home to support its inhabitants, the recognition sys- tem needs to accurately learn from the observations acquired through sensors, which are installed in the home. Given a predefined set of the inhabitant's daily activities, the question is which sensors are important to accurately recognise these activities. This paper addresses the sensor selection problem through a filter-based approach, which is based on information gain. We evaluate the effectiveness of the proposed method on two publicly available smart home datasets.

[1]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[2]  Yuhuang Zheng,et al.  Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework , 2015, J. Electr. Comput. Eng..

[3]  Shah Atiqur Rahman,et al.  Unintrusive eating recognition using Google Glass , 2015, 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).

[4]  Evangelos E. Milios,et al.  Detection of daily living activities using a two-stage Markov model , 2013, J. Ambient Intell. Smart Environ..

[5]  Diane J. Cook,et al.  Energy Prediction in Smart Environments , 2010, Intelligent Environments.

[6]  Lawrence B. Holder,et al.  Sensor selection to support practical use of health‐monitoring smart environments , 2011 .

[7]  Gwenn Englebienne,et al.  Accurate activity recognition in a home setting , 2008, UbiComp.

[8]  Younghwan Yoo,et al.  User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm , 2015, Sensors.

[9]  Hans W. Guesgen,et al.  Unsupervised Learning of Human Behaviours , 2011, AAAI.

[10]  Muttukrishnan Rajarajan,et al.  Activity Recognition in Smart Homes Using Clustering Based Classification , 2014, 2014 22nd International Conference on Pattern Recognition.

[11]  Chris D. Nugent,et al.  Feature Sub-set Selection for Activity Recognition , 2015, ICOST.

[12]  Gregory J. Pottie,et al.  Feature selection based on mutual information for human activity recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).