Change Point Detection Using Multivariate Exponentially Weighted Moving Average (MEWMA) for Optimal Parameter in Online Activity Monitoring

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In recent years, wearable sensors are integrating frequently and rapidly into our daily life day by day. Such smart sensors have attracted a lot of interest due to their small sizes and reasonable computational power. For example, body worn sensors are widely used to monitor daily life activities and identify meaningful events. Hence, the capability to detect, adapt and respond to change performs a key role in various domains. A change in activities is signaled by a change in the data distribution within a time window. This change marks the start of a transition from an ongoing activity to a new one. In this paper, we evaluate the proposed algorithm’s scalability on identifying multiple changes in different user activities from real sensor data collected from various subjects. The Genetic algorithm (GA) is used to identify the optimal parameter set for Multivariate Exponentially Weighted Moving Average (MEWMA) approach to detect change points in sensor data. Results have been evaluated using a real dataset of 8 different activities for five different users with a high accuracy from 99.2 % to 99.95 % and G-means from 67.26 % to 83.20 %.
Original languageEnglish
Title of host publicationUCAmI 2016: Ubiquitous Computing and Ambient Intelligence
Place of PublicationSpain
Number of pages9
ISBN (Electronic)978-3-319-48746-5
Publication statusPublished - 2 Nov 2016
Event10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran - Gran Canaria, Spain
Duration: 29 Nov 20162 Dec 2016


Conference10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran
Internet address



  • Multiple change points
  • Activity monitoring
  • Genetic algorithm
  • Accelerometer

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