Unsupervised human activities recognition
Human Activity Recognition (HAR) is an important component in assistive technologies, however, we have not seen wide adoption of HAR technologies in our homes. Two main hurdles to the wide adoption of HAR technologies in our homes are the expensive infrastructure requirement and the use of supervised learning in the HAR technologies. Many HAR researches have been carried out assuming an environment embedded with sensors. In addition, the majority of HAR technologies use supervised approaches, where there are labeled data to train the expert system. In reality, our natural living environment are not embedded with sensors. Labeled data are not available in our natural living environment. We are developing a framework for autonomous HAR suitable in our natural living environment, i.e. the sensor-less homes. The framework uses unsupervised learning approach to enable a robot, acting as a mobile sensor hub, to autonomously collect data and learn the different human activities without requiring manual (human) labeling of the data.
Application invited for: MSc, PhD