The world’s population is ageing, increasing the awareness of neurological and behavioral 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 behavior. The available technologies to monitor human behavior 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 behavior 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 behavior 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 behaviors 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 behavior were continuously quantified by an anomaly detection algorithm, where distance measurements were used to detect significant changes in behavior. Experimental results demonstrate the effectiveness of the proposed framework that revealed an increase potential to learn behavior patterns and detect anomalies.
Author: Letícia Fernandes
Type: MSc thesis
Partner: Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa