Learning Human Behaviour Patterns By Trajectory And Activity Recognition

The world's population is ageing, increasing the awareness of neurological and behavioural impairments that may arise from the human ageing. These impairments can be manifested by cognitive conditions or mobility reduction. These conditions are difficult to be detected on time, there is a lack of routine screening which demands the development of solutions to better assist and monitor human behaviour. This study investigates the question of what we can learn about human behaviour patterns from the rich and pervasive mobile sensing data. Data was collected over 6 months, measuring two different human routines through human trajectory analysis and activity recognition comprising indoor and outdoor environment. A framework for modelling human behaviour was developed using human motion features, extracted with and without previous knowledge of the user's behaviour. The human patterns were modelled through probability density functions and clustering approaches. Using the learned patterns, inferences about the current human behaviour were continuously quantified by an anomaly detection algorithm where distance measurements were used to detect significant changes in behaviour. Experimental results demonstrate the effectiveness of the proposed framework that revealed an increased potential to learn behavioural patterns and detect anomalies.