Statistical learning of mobility patterns from long-term monitoring of locomotor behaviour with body-worn sensors

Ghosh, Sayantan and Fleiner, Tim and Giannouli, Eleftheria and Jaekel, Uwe and Mellone, Sabato and Häussermann, Peter and Zijlstra, Wiebren. (2018) Statistical learning of mobility patterns from long-term monitoring of locomotor behaviour with body-worn sensors. Scientific Reports, 8 (1). p. 7079.

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Official URL: https://edoc.unibas.ch/76468/

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Long term monitoring of locomotor behaviour in humans using body-worn sensors can provide insight into the dynamical structure of locomotion, which can be used for quantitative, predictive and classification analyses in a biomedical context. A frequently used approach to study daily life locomotor behaviour in different population groups involves categorisation of locomotion into various states as a basis for subsequent analyses of differences in locomotor behaviour. In this work, we use such a categorisation to develop two feature sets, namely state probability and transition rates between states, and use supervised classification techniques to demonstrate differences in locomotor behaviour. We use this to study the influence of various states in differentiating between older adults with and without dementia. We further assess the contribution of each state and transition and identify the states most influential in maximising the classification accuracy between the two groups. The methods developed here are general and can be applied to areas dealing with categorical time series.
Faculties and Departments:03 Faculty of Medicine
03 Faculty of Medicine > Departement Sport, Bewegung und Gesundheit > Bereich Sport- und Bewegungsmedizin
UniBasel Contributors:Giannouli, Eleftheria
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Nature Publishing Group
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:05 Nov 2020 13:23
Deposited On:05 Nov 2020 13:23

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