Paper Info
Title
Prediction of mobility entropy in an Ambient Intelligent environment
Abstract
Ambient Intelligent (AmI) technology can be used to help older adults to live longer and independent lives in their own homes. Information collected from AmI environment can be used to detect and understand human behaviour, allowing personalized care. The behaviour pattern can also be used to detect changes in behaviour and predict future trends, so that preventive action can be taken. However, due to the large number of sensors in the environment, sensor data are often complex and difficult to interpret, especially to capture behaviour trends and to detect changes over the long-term. In this paper, a model to predict the indoor mobility using binary sensors is proposed. The model utilizes weekly routine to predict the future trend. The proposed method is validated using data collected from a real home environment, and the results show that using weekly pattern helps improve indoor mobility prediction. Also, a new measurement, Mobility Entropy (ME), to measure indoor mobility based on entropy concept is proposed. The results indicate ME can be used to distinguish elders with different mobility and to see decline in mobility. The proposed work would allow detection of changes in mobility, and to foresee the future mobility trend if the current behaviour continues.
Year
DOI
Venue
2014
10.1109/IA.2014.7009460
Intelligent Agents
Keywords
Field
DocType
ambient intelligence,mobile computing,neural nets,AmI environment,ambient intelligent environment,binary sensors,indoor mobility,mobility entropy prediction,neural network
Intelligent environment,Data mining,Behaviour pattern,Binary sensors,Computer science,Mobility model,Mobility prediction,Hidden Markov model,Preventive action,Market research
Conference
Citations 
PageRank 
References 
1
0.37
8
Authors
3
Name
Order
Citations
PageRank
Saisakul Chernbumroong11315.76
Lofti A. Zadeh2145273847.07
Caroline Langensiepen3616.12