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Using multi-sensor tracking data to analyze the mobility and activity behavior of older adults


Allahbakhshi, Hoda; Weibel, Robert (2017). Using multi-sensor tracking data to analyze the mobility and activity behavior of older adults. In: 4th AGILE PhD School, Leeds UK, 30 October 2017 - 2 November 2017.

Abstract

The increase in the older adult population has been occurring at unprecedented and accelerating rates in recent decades.Healthy ageing as the process of maintaining the functional mobility is therefore important. In order to understand how functional mobility in daily life is associated with health in older adults, such behaviour needs to be studied in real-life conditions, which can be done using sensor-based ambulatory assessment methods. The aim of this study is to contribute to developing a full, individualized description of human mobility behavior considering different spatio-temporal patterns, and link such personal mobility profiles to psychological resources available to an individual. The participants are healthy older adults above 65 years old from MOASIS study, who will collect the data during 4 weeks of their everyday life. The multi-sensor data will be used for the movement analysis. Pattern recognition and classification algorithms are proposed methodologies to achieve the aim of this study. This paper is quite useful for understanding of individual movement through the use of new sensing devices.

Abstract

The increase in the older adult population has been occurring at unprecedented and accelerating rates in recent decades.Healthy ageing as the process of maintaining the functional mobility is therefore important. In order to understand how functional mobility in daily life is associated with health in older adults, such behaviour needs to be studied in real-life conditions, which can be done using sensor-based ambulatory assessment methods. The aim of this study is to contribute to developing a full, individualized description of human mobility behavior considering different spatio-temporal patterns, and link such personal mobility profiles to psychological resources available to an individual. The participants are healthy older adults above 65 years old from MOASIS study, who will collect the data during 4 weeks of their everyday life. The multi-sensor data will be used for the movement analysis. Pattern recognition and classification algorithms are proposed methodologies to achieve the aim of this study. This paper is quite useful for understanding of individual movement through the use of new sensing devices.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:2 November 2017
Deposited On:26 Dec 2018 18:29
Last Modified:17 Sep 2019 19:50
Publisher:s.n.
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://ceur-ws.org/Vol-2088/

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