Human Activity Recognition with Wireless Sensor Networks Using Active Learning

In the last decade, Wireless Sensor Networks (WSNs) appeared as one of the emerging technologies that combine automated sensing, embedded computing and wireless networking into tiny embedded devices. This evolution not only enabled the use of miniaturized wireless sensors ranging from simple temperature sensors to more complex sensors that can monitor health status, but also enabled the use of WSNs in various applications ranging from military applications to healthcare. Monitoring human activities with multi-modal sensors can be used in different application areas. Medical applications, home monitoring and assisted living are some of the most prominent application domains. The aim of this project is to develop a WSN system that can enable the monitoring of daily activities of its users without disturbing their daily routine. The differentiator characteristics of the project are:

  • processing of multi-modal sensors data and inference using machine learning,
  • leaning the user behaviors through methods such as active learning and supporting the user in the case of drifts from their daily routines,
  • enabling a lifelong-learning and user specific system
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