SPEED: An Inhabitant Activity Prediction Algorithm for Smart Homes

This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on -off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5.

[1]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[2]  Chia-Feng Juang,et al.  Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[3]  Hani Hagras,et al.  A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Michael C. Mozer,et al.  The Neural Network House: An Environment that Adapts to its Inhabitants , 1998 .

[5]  Diane J. Cook,et al.  Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm , 2007, IEEE Intelligent Systems.

[6]  Abraham Lempel,et al.  Compression of individual sequences via variable-rate coding , 1978, IEEE Trans. Inf. Theory.

[7]  Chen Wu,et al.  User-Centric Environment Discovery With Camera Networks in Smart Homes , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Brian D. Davison,et al.  Predicting Sequences of User Actions , 1998 .

[9]  Diane J. Cook,et al.  Keeping the Resident in the Loop: Adapting the Smart Home to the User , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[10]  Diane J. Cook,et al.  Data Mining for Hierarchical Model Creation , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Sajal K. Das,et al.  LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS Networks , 2002, Wirel. Networks.

[12]  Stathes Hadjiefthymiades,et al.  Advanced Inference in Situation-Aware Computing , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[13]  Li-Chen Fu,et al.  Design and Realization of a Framework for Human–System Interaction in Smart Homes , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[14]  Ian H. Witten,et al.  Data Compression Using Adaptive Coding and Partial String Matching , 1984, IEEE Trans. Commun..