A Continuous-Time Model-Based Approach to Activity Recognition for Ambient Assisted Living

  • Laura Carnevali
  • Christopher Nugent
  • Fulvio Patara
  • Enrico Vicario
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9259)


In Ambient Assisted Living (AAL), Activity Recognition (AR) plays a crucial role in filling the semantic gap between sensor data and interpretation needed at the application level. We propose a quantitative model-based approach to on-line prediction of activities that takes into account not only the sequencing of events but also the continuous duration of their inter-occurrence times: given a stream of time-stamped and typed events, online transient analysis of a continuous-time stochastic model is used to derive a measure of likelihood for the currently performed activity and to predict its evolution until the next event; while the structure of the model is predefined, its actual topology and stochastic parameters are automatically derived from the statistics of observed events. The approach is validated with reference to a public data set widely used in applications of AAL, providing results that show comparable performance with state-of-the-art offline approaches, namely Hidden Markov Models (HMM) and Conditional Random Fields (CRF).


Ambient Assisted Living (AAL) Activity Recognition (AR) Continuous-time stochastic models Transient analysis On-line prediction Process enhancement 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laura Carnevali
    • 1
  • Christopher Nugent
    • 2
  • Fulvio Patara
    • 1
  • Enrico Vicario
    • 1
  1. 1.Department of Information EngineeringUniversity of FlorenceFlorenceItaly
  2. 2.School of Computing and MathematicsUniversity of UlsterBelfastUK

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