1,505 research outputs found
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, relying only on the periodic medical appointments. Therefore, there is
a lack of routine screening which demands the development of solutions to better assist
and monitor human behaviour. The available technologies to monitor human behaviour
are limited to indoors and require the installation of sensors around the user’s homes
presenting high maintenance and installation costs. With the widespread use of smartphones,
it is possible to take advantage of their sensing information to better assist the
elderly population. This study investigates the question of what we can learn about human
pattern behaviour from this rich and pervasive mobile sensing data. A deployment
of a data collection over a period of 6 months was designed to measure three 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 in an unsupervised and supervised
manner. The unsupervised feature extraction is able to measure mobility properties such
as step length estimation, user points of interest or even locomotion activities inferred
from an user-independent trained classifier. The supervised feature extraction was design
to be user-dependent as each user may have specific behaviours that are common to
his/her routine. The human patterns were modelled through probability density functions
and clustering approaches. Using the human 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 increase potential to learn behaviour patterns and detect anomalies
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