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Vol. 218, Issue 12, December 2017, pp. 1-9




Robust Network Models for Mobility Analysis: Validation
from an Unstable Mobility in a Maritime Setting

Ik-Hyun Youn, Abhilash Patella and * Hesham H. Ali

College of Information Science and Technology, Omaha, NE 68182, USA
Tel.: +1 402 554 2380, fax: +1 402 554 3400

E-mail: hali@unomaha.edu


Received: 10 November 2017 /Accepted: 10 December 2017 /Published: 29 December 2017

Digital Sensors and Sensor Sysstems


Abstract: The importance of human mobility in maintaining physical health is of emerging interest in research and practice. Technological advances in wearable technology enable us to monitor human mobility in out-of-laboratory settings. Although a large amount of human mobility data is available from wearable sensors, there is a lack of systematic methodologies for extracting useful knowledge on human mobility from the collected data. The objective description of the different status of mobility patterns to interpret different physical health levels especially remains challenging. In this paper, robust network modeling from our preliminary study is validated in a real-world scenario with stable and unstable mobility conditions. The models based on population analysis utilize mobility data and extract distinctive mobility characteristics. Correlation networks and population-based analysis are utilized to efficiently examine the natural variability of human movement. Results demonstrate that the proposed robust network models identify mobility pattern changes in maritime conditions.


Keywords: Mobility parameters, Population-based analysis, Physical health assessment.


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