Continuous activity recognition with missing data

Human activity recognition involves several problems like changes when an activity is performed by different persons. This means that people can perform the same activity faster or slower and also the way that an activity is performed can change, therefore we can have different trajectories representing the same activity. Another problem exists when we do not have the whole trajectory because of occlusion or noise. In this work, an approach for human activity recognition based on the Fourier transform and Bayesian networks is presented. This approach can recognize activities performed at different velocities by different people and can work with missing data. It performs continuous activity recognition without the necessity of manually indicating when the activity starts or finishes.