Prediction of mobility entropy in an ambient intelligent environment

Chernbumroong, S., Lotfi, A. ORCID: 0000-0002-5139-6565 and Langensiepen, C. ORCID: 0000-0002-0165-9048, 2014. Prediction of mobility entropy in an ambient intelligent environment. In: IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014), Orlando, Florida, 9-12 December 2014, Orlando, Florida.


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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 understanding 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.

Item Type: Conference contribution
Creators: Chernbumroong, S., Lotfi, A. and Langensiepen, C.
Date: 2014
Divisions: Schools > School of Science and Technology
Record created by: EPrints Services
Date Added: 09 Oct 2015 10:21
Last Modified: 09 Jun 2017 13:27

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