Authors:
Letícia Fernandes
1
;
Marília Barandas
1
and
Hugo Gamboa
1
;
2
Affiliations:
1
Associação Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
;
2
Laboratório de Instrumentação, Engenharia Biomédica e Física da Radiação (LIBPhys-UNL), Departamento de Física, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte da Caparica, 2829-516 Caparica, Portugal
Keyword(s):
Human Behaviour, Pattern Recognition, Anomaly Detection, Ambient Assisted Living, Probability Density Function, Clustering.
Abstract:
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 p
atterns, 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.
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