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Global Positioning System: Signals, Measurements, And Performance (Revised Second Edition) Books Pdf

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Global Positioning System: Signals, Measurements, And Performance (Revised Second Edition) Books Pdf

This is a key study to show that high-sensitivity GPS can be used to study ionospheric processes in the mid-latitude region where VLF (very low frequency) is not available and to determine the upper limit of the diurnal variation in tropospheric electron density. This study demonstrates the potential of high-sensitivity GPS for monitoring short-term variations in tropospheric electron density and shows a site-specific low-frequency signal dominated by tropospheric ionospheric processes.

There is a limited number of studies involving the interaction of GPS technology with other sensors; a recent example is the GPS accelerometer used to measure the physical activities of the users themselves. Recently, researchers used a wearable wrist-based GPS receiver to monitor the walking activities of busy commuters in an urban setting [ 28 ]. In a study of the multi-modal tracking of physical activity in two Hong Kong neighborhoods, the GPS data of a new generation GPS receiver was combined with accelerometry data to determine in-depth physical activity patterns and the environment exposure [ 63 ]. The inclusion of the accelerometer data along with GPS technology enhanced the data accuracy of the GPS receiver by about 2 to 4 meters at a maximum speed of 15 kilometers per hour. Combining the two sets of data allowed for more detailed and improved assessment of human exposure to the environments of the city. This multi-modal approach has been used successfully in environmental assessment research to provide time-location exposure information [ 10, 64, 65 ].

Wearable devices for active health monitoring have become popular for human physical activity tracking in recent years. These wearable devices provide accurate and real-time location and heart rate information to healthcare providers or in automated systems, such as smart watches, that provide more precise health-related information such as the current health status of the user. Examples of commercially available wearable devices include the Fitbit Ultra, the JawboneUP, the Nike+ Fuelband, and the health-monitoring Jawbone Inc. smartphone application. Moreover, the smartphone GPS receiver can work as a stand-alone wearable device and enable the user to track their own physical activity throughout the day. GPS data from smartphones can provide useful information about physical activity patterns and travel distances. GPS technology can also be combined with accelerometers to obtain more detailed information about human movement. This combination of wearable devices allows the individual the ability to automatically track their physical activity and activity patterns throughout the day without user intervention, thus making it easier to record and analyze long-term health behavior changes as a result of physical activity. These devices track the location of the user as well as the amount of physical activity and activities undertaken (such as walking, running, and cycling). Users can add information to their profile using the device such as the number of steps taken, calories burned, or distance traveled. A variety of application programs can be downloaded to the smartphone to report the metrics of physical activity, including the number of steps, distance traveled and heart rate. This technology has also been used in studies of exercise physiology and nutrition, examining the intensity of exercise and post-exercise recovery [ 36, 38 ].

Every location data set, whether obtained by random, opportunistic or geocoded activities, has the potential to reveal aspects of human behavior that society would prefer to remain unexposed. The location component of Human Phenome projects will increase our knowledge of variation in disease, and develop the information required to incorporate location in biomedical and population research. The ability to integrate GPS information from across the health research spectrum will provide a unique and unparalleled opportunity to track human behavior and move our understanding of the determinants of human health beyond traditional demographic considerations and into a new era of population science.
This work has been carried out within the PREDICT project. PREDICT ( http://www.predicteu.org/ ) is funded by the European Commission’s FP7 Health Program (Grant HEALTH-F3-2009-223386). The content of this paper only reflects the views of the authors and the Funder and should not be taken to represent the position of the Agency or ECDC.
An application for government-funded researchers using GPS data is to assess risk of specific health events (e.g., infant mortality, diabetes, or accidents) in certain places (e.g., dense vs. sparse, urban vs. rural). However, if association findings are not adjusted for individual-level factors, these factors will be erroneously attributed to the place (e.g., socioeconomic status). Therefore, researchers need to adjust the association of a place (or region) with a specific health outcome for individual-level factors. For example, a 7 % lower risk of mortality for the elderly will be erroneously attributed to an urban area if the unadjusted association of urban areas with mortality was 3 %. Alternatively, if the unadjusted association of urban areas with mortality was -3 %, then the adjusted association of urban areas with mortality will be 7 % (3-3 = 7%). More generally, we defined an individual-level adjusted association of place with disease risk, as the difference in the log odds of the disease of interest for a given health condition between a subject’s actual residence and any other location. This method can be used to examine the association of place with disease risk without accounting for individual-level factors that would artificially inflate the association. As an illustration, we used GPS information to examine the association of the adult mortality rate with the density of commercial and industrial development. Our outcome was the person-years mortality rate of individuals aged 15–64, and the exposures were the densities of commercial and industrial development at the census tract level. We obtained the densities of commercial and industrial development from a publicly available dataset [ 52 ]. The correlation of the densities of commercial and industrial development and the adult mortality rate was 0.73. The adjusted odds of mortality for each of the densities of commercial and industrial development relative to the odds at the reference density were calculated and plotted in a map, with colors representing the odds ratios (see Figure 4).


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