title: A time series based sequence prediction algorithm to detect activities of daily living in smart home.
abstract: OBJECTIVE
  The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included.
  
  
  METHODS
  The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge.
  
  
  RESULTS
  The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED.
  
  
  CONCLUSIONS
  Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.
authors.affiliation: M. Marufuzzaman, Dept. of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia, E-mail: marufsust@gmail.com.
authors.forename: M
authors.initials: M
authors.lastname: Marufuzzaman
authors.forename: M B I
authors.initials: MB
authors.lastname: Reaz
authors.forename: M A M
authors.initials: MA
authors.lastname: Ali
authors.forename: L F
authors.initials: LF
authors.lastname: Rahman
mesh_terms: D000203:Activities of Daily Living; D000465:Algorithms; D005544:Forecasting; D006798:Housing; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D012985:Software Design
keywords: ADL; M-SPEED; machine learning; sequence prediction; smart home