Ensemble classifier of long short-term memory with fuzzy temporal windows on binary sensors for activity recognition

Javier Medina-Quero, Shuai Zhang, CD Nugent, Macarena Espinilla

Research output: Contribution to journalArticle

15 Citations (Scopus)
25 Downloads (Pure)


There are approaches that successfully recognize activities of daily living by using a trained classifier on feature vectors created from binary sensor data. Although these approaches have been successful, there are still open issues such as the evaluation of multiple temporal windows, ensembles of classifiers or unbalanced classes which need to be addressed in order to improve the performance of the real-time activity recognition process. In this paper, we present a methodology for Real-Time Activity Recognition based on the diverse fields of Machine Learning, including Fuzzy Logic and Recurrent Neural Networks. The methodology uses a long-term and short-term representation of binary-sensor activations based on Fuzzy Temporal Windows. The paper proposes an ensemble of activity-based classifiers for the purposes of balanced training, where each classifier in the ensemble is a Long Short-Term Memory. The approach was evaluated using two binary-sensor datasets of daily living activities and benchmarked against previous approaches based on the combination of sensor activation features.
Original languageEnglish
Pages (from-to)441-453
Number of pages13
JournalExpert Systems with Applications
Early online date1 Aug 2018
Publication statusE-pub ahead of print - 1 Aug 2018


  • Activity recognition
  • Fuzzy temporal windows
  • Long short-term memory
  • Unbalanced data
  • Ensemble architectures

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