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      Global positioning system: a new opportunity in physical activity measurement

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          Accurate measurement of physical activity is a pre-requisite to monitor population physical activity levels and design effective interventions. Global Positioning System (GPS) technology offers potential to improve the measurement of physical activity. This paper 1) reviews the extant literature on the application of GPS to monitor human movement, with a particular emphasis on free-living physical activity, 2) discusses issues associated with GPS use, and 3) provides recommendations for future research. Overall findings show that GPS is a useful tool to augment our understanding of physical activity by providing the context (location) of the activity and used together with Geographical Information Systems can provide some insight into how people interact with the environment. However, no studies have shown that GPS alone is a reliable and valid measure of physical activity.

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          Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions.

          Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.
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            Bicycling for transportation and health: the role of infrastructure.

             Jennifer Dill (2008)
            This paper aims to provide insight on whether bicycling for everyday travel can help US adults meet the recommended levels of physical activity and what role public infrastructure may play in encouraging this activity. The study collected data on bicycling behavior from 166 regular cyclists in the Portland, Oregon metropolitan area using global positioning system (GPS) devices. Sixty percent of the cyclists rode for more than 150 minutes per week during the study and nearly all of the bicycling was for utilitarian purposes, not exercise. A disproportionate share of the bicycling occurred on streets with bicycle lanes, separate paths, or bicycle boulevards. The data support the need for well-connected neighborhood streets and a network of bicycle-specific infrastructure to encourage more bicycling among adults. This can be accomplished through comprehensive planning, regulation, and funding.
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              Geographically masking health data to preserve confidentiality.

              The conventional approach to preserving the confidentiality of health records aggregates all records within a geographical area that has a population large enough to ensure prevention of disclosure. Though this approach normally protects the privacy of individuals, the use of such aggregated data limits the types of research one can conduct and makes it impossible to address many important health problems. In this paper we discuss the design and implementation of geographical masks that not only preserve the security of individual health records, but also support the investigation of questions that can be answered only with some knowledge about the location of health events. We describe several alternative methods of masking individual-level data, evaluate their performance, and discuss both the degree to which we can analyse masked data validly as well as the relative security of each approach, should anyone attempt to recover the identity of an individual from the masked data. We conclude that the geographical masks we describe, when appropriately used, protect the confidentiality of health records while permitting many important geographically-based analyses, but that further research is needed to determine how the power of tests for clustering or the strength of other associative relationships are adversely affected by the characteristics of different masks.

                Author and article information

                Int J Behav Nutr Phys Act
                The International Journal of Behavioral Nutrition and Physical Activity
                BioMed Central
                4 November 2009
                : 6
                : 73
                [1 ]Clinical Trials Research Unit, University of Auckland, Auckland, New Zealand
                Copyright © 2009 Maddison and Ni Mhurchu; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


                Nutrition & Dietetics


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