Global Positioning System Tracking Research Articles

Effects of high-carbohydrate versus mixed-macronutrient meals on female soccer physiology and performance

Athletes are often told to minimize intakes of fiber, fat, and protein in pre-competition meals to avoid gut distress, but this guidance is based on scant direct evidence. This study evaluated the physiological and perceptual effects of pre-competition mixed-macronutrient (MM) and high-carbohydrate (HCHO) meals in collegiate female soccer players. Fifteen players participated in this randomized, investigator-blinded, crossover study involving two ~ 1000-kcal meals (HCHO and MM) consumed 4h prior to 70-min scrimmages. Assessments included global positioning system (GPS) tracking, heart rate (HR), perceived exertion (RPE), ratings of fatigue (ROF), gut symptoms, and perceptions of satiety, hunger, and fullness. Differences between conditions for HR, RPE, ROF, and gut symptoms were evaluated with Wilcoxon signed-rank tests. GPS data and hunger, satiety, and fullness scores were compared using within-subjects repeated measures ANOVAs. No statistically significant differences were found between the conditions at any time point for HR, RPE, ROF, or gut symptoms. Significant time effects were found for two GPS variables (total distance covered and high-speed running), indicating that participants covered less distance during the second half of the scrimmages in comparison to the first half. However, there were no significant condition or condition × time interactions for GPS data. Finally, there were no condition or condition × time interactions for hunger, fullness, and satiety, though significant time effects were observed. A MM meal consumed 4h prior to 70min of soccer competition does not increase gut symptoms and can be similarly ergogenic as a HCHO meal.

Outdoor life in dementia: How predictable are people with dementia in their mobility?

IntroductionPeople with dementia (PWD) often become disoriented, which increases their risk of getting lost. This article explores the extent to which we can predict future whereabouts of PWD by learning from their past mobility patterns using Global Positioning System (GPS) tracking devices.MethodsSeven older adults with dementia and eight healthy older adults completed 8 weeks of GPS data collection. We computed the probability that an appropriate algorithm can correctly predict the participant's future destinations using spatial and temporal patterns in each participant's GPS trajectories.ResultsRelying on both spatial and temporal patterns, our results suggest that a 4‐week record of mobility patterns displays 95% potential predictability across the dementia group, which is significantly higher than 92% potential predictability among the controls, t(13) = –3.39, P < .01, d = –1.75. That is, we can hope to be able to predict destinations of PWD about 95% of the time and destinations of controls about 92% of the time.DiscussionsOur findings on predictability of mobility patterns among PWD offer new perspectives on predictive mobility models that can be used to locate missing persons with dementia.

GPS Tracking Technologies to Measure Mobility-Related Behaviors in Community-Dwelling Older Adults: A Systematic Review.

Global positioning system (GPS) technology has been increasingly used in aging research as a tool for reliably capturing the level and patterns of mobility among older adults. This article aims to systematically review the current state of GPS-based mobility research with community-dwelling older adults. Twenty-nine studies from 2008 to 2019 are included. Included studies examined various forms of temporal and spatial mobility measures. This review provides a synthesis of the current evidence on the risk factors or correlates of GPS-driven mobility limitations, such as demographic, cognitive, physical, psychological, and environmental factors. There is variability in types of GPS technology and GPS-derived mobility measures, GPS recording methods, data processing, and correlates of mobility limitation across studies. Future research should focus on identifying meaningful GPS-derived mobility measures and developing standardized protocols for GPS administration and data analytics for comparison across studies.

Mountain Top-Based Atmospheric Radio Occultation Observations with Open/Closed Loop Tracking: Experiment and Validation

Through Global Navigation Satellite System (GNSS) occultation measurement, the global ionosphere and atmosphere can be observed. When the navigation satellites’ signal passes through the lower atmosphere, the rapid change of the atmospheric refractive index gradient will cause serious multipath phenomena in radio wave propagation. Atmospheric doppler frequency shift and amplitude signal fluctuations increase drastically. Due to the attenuation of signal amplitude and the rapid change of the Doppler frequency, the general phase locked loop (PLL) cannot work properly. Hence, a more stable tracking technology is needed to track the occultation signal passing through the lower atmosphere. In this paper, a mountain-top based radio occultation experiment is performed, where we employ an open-loop receiver and remove the navigation bits by the internal demodulation. In the process of the experiment, we adopt the open-loop tracking technique and there is no feedback between the observed signal and the control model. Specifically, taking the pseudo-range and doppler information from models as input, three key parameters, i.e., accurate code phase, carrier doppler and code doppler, can be obtained, and furthermore, the accurate accumulation is determined by them. For the full open-loop occultation data, a closed-loop observation assisted strategy is presented to compare the tracking results between open-loop and closed-loop occultation data. Through the compared results, we can determine whether the initial phase has been reversed or not, and obtain the high consistency corrected open-loop data that can be directly used for subsequent atmospheric parameters inversion. To verify the effect of open-loop tracking and open-loop inversion, we used the company’s self-developed occult receiver system for verification. The company’s self-developed occult receiver system supports Global Position System (GPS)/Beidou satellites constellation (BD, the 2nd and 3rd generations) dual systems. We have verified GPS and BD open-loop tracking and inversion, carried out in a three-week mountain-based experiment. We used closed-loop and open-loop strategies to track and capture the same navigation star to detect its acquisition effect. Finally, we counted the results of a week (we only listed the GPS data; BD’s effect is similar). The experimental results show that the open-loop has expanded the signal-cut-off angle by nearly 20% under the condition of counting all angles, while the open-loop has increased the signal-cut-off angle value by nearly 89% when only calculating the negative angle. Finally, the atmosphere profiles retrieved from observations in open-loop tracking mode are evaluated with the local observations of temperature, humidity and pressure provided by the Beijing Meteorological Bureau, and it is concluded that the error of open-loop tracking method is within ~4% in MSER (mean square error of relative error), which meets the accuracy of its applications (&lt;5%, in MSER).

Emigration and First-Year Movements of Initial Wolf Translocations to Isle Royale

Canis lupis (Gray Wolf or Wolf) can move great distances with important consequences to the persistence of isolated populations. We used global position system (GPS) tracking technology to summarize the movements of 4 Wolves translocated to Isle Royale National Park, MI, in 2018–2019. We quantified aspects of movement behavior of individual Wolves following translocation to, and an emigration event from the Isle Royale ecosystem.

Sustainability of Public Transportation: An Examination of User Behavior to Real-Time GPS Tracking Application

Public transportation is an effective method of mobility that promotes cost-saving and is environmentally friendly. Poor public transport ridership in Malaysia is due to the unsatisfactory attitude of public transport users and inaccurate information on departure and arrivals. Sarawak, a state of Malaysia, is especially poor in ridership of public transport. A real-time Global Positioning System (GPS) tracking application (app) was found to be an effective tool to increase the ridership of public transport. Hence, a mobile app named UniBus was developed to enhance the ridership of public transport in Sarawak. The determinants that affect satisfaction and customer loyalty such as accessibility, reliability, comfort, safety, and security were all examined before and after the use of real-time GPS tracking app. The data was collected in Kuching, and targeted public transport users who used the UniBus app. The result indicated that all the mentioned variables were improved after using a real-time GPS tracking app. It is suggested that future studies can consider other factors such as service quality, availability, and perceived value as well as cover other states of Malaysia.

Linking physical and social environments with mental health in old age: a multisensor approach for continuous real-life ecological and emotional assessment

BackgroundUrban stress is mentioned as a plausible mechanism leading to chronic stress, which is a risk factor of depression. Yet, an accurate assessment of urban stressors in environmental epidemiology requires...

Urban gulls adapt foraging schedule to human‐activity patterns

Numerous animals are able to adapt to temporal patterns in natural food availability, but whether species living in relatively novel environments such as cities can adapt to anthropogenic activity cycles is less well understood. We aimed to assess the extent to which urban gulls have adapted their foraging schedule to anthropogenic food source fluctuations related to human activity by combining field observations at three distinct urban feeding grounds (park, school and waste centre) with global positioning system (GPS) tracking data of gulls visiting similar types of feeding grounds throughout the same city. We found that the birds’ foraging patterns closely matched the timing of school breaks and the opening and closing times of the waste centre, but gull activity in the park appeared to correspond to the availability of natural food sources. Overall, this suggests that gulls may have the behavioural flexibility to adapt their foraging behaviour to human time schedules when beneficial and that this trait could potentially enable them to thrive in cities.

Traffic travel pattern recognition based on sparse Global Positioning System trajectory data

This article mainly uses sparse Global Positioning System trajectory data to identify traffic travel pattern. In this article, the data are preprocessed and the eigenvalues are calculated. Then, the Global Positioning System track points are identified and extracted by walking and non-walking segments. Finally, the three machine learning models of support-vector machine, decision tree, and convolutional neural network are used for comparison experiments. The innovation of this article is to propose a walking and non-walking identification method based on density-based spatial clustering of applications with noise clustering. The method takes into account the continuous state between the geographical distributions, and it has better noise immunity against the influence of external factors. In this process, this article directly achieves better conversion point recognition results through the Global Positioning System track point information, which lays a good foundation for the accuracy of travel pattern recognition. The experimental results of this article show that compared with threshold-based and multi-layer perceptron–based methods, the recognition method based on density-based spatial clustering of applications with noise clustering has the highest accuracy, reaching 82.20%. Then, support-vector machine, decision tree, and convolutional neural network are used for traffic travel pattern recognition. The F1-score of support-vector machine is the highest, reaching 0.84, and the F1-scores of decision tree and convolutional neural network are 0.78 and 0.80, respectively. Finally, the support-vector machine was used for the recognition test to achieve an accuracy of 76.83%.

Spatiotemporal analyses of tracking data reveal fine-scale, daily cycles in seabird–fisheries interactions

Abstract Human fisheries provide scavengers with abundant and predictable feeding opportunities that may schedule their behavioural patterns. Using miniaturized global positioning system (GPS) tracking technology, we evaluated how Audouin’s gull (Ichthyaetus audouinii), a Mediterranean endemic seabird that makes extensive use of feeding opportunities provided by fisheries, co-occurred (i.e. presumably interacted) with the most important fishing fleets operating off the NE Iberian Peninsula (i.e. diurnal trawlers and nocturnal purse seiners), both in space and time. Results showed that individuals were able to adapt their distribution and activity patterns to the scheduled routines of these fisheries. Waveform analyses based on co-occurring positions revealed that most interactions with trawlers occurred during the afternoon (16:00 h GMT + 1) when discarding occurs as vessels return to port. In contrast, gull-purse seiner interactions largely occurred at night (between 02:00 and 04:00 h) coinciding with the setting and hauling of the nets. Moreover, we found an individual component in seabird–fishery interactions, showing that there may be differential use of fisheries by individuals within the population. In addition to implications for our understanding of the behavioural ecology of this species, these results may have important management implications, particularly under the current European Union Common Fisheries Policy scenario of largely restricting discards.

Improving Search Quality in Crowdsourced Bib Number Tagging Systems Using Data Fusion

Today, crowd computing is successfully applied for many information processing problems in a variety of domains. One of the most acute issues with crowd-powered systems is the quality of results (as humans can make errors). Therefore, a number of methods have been proposed to process the results obtained from the crowd in order to compensate human errors. Most of the existing methods of processing contributions are constructed based on a (natural) assumption that the only information available is unreliable data obtained from the crowd. However, in some cases, additional information is available, and it can be utilized in order to improve the overall quality of the result. The paper describes a crowd computing application for community tagging of running race photos. It presents a utility analysis to identify situations in which community photo tagging is a reasonable choice. It also proposes a data fusion model making use of runners’ location information recorded in their Global Positioning System (GPS) tracks. Field experiments with the applications show that community-based tagging can collect enough contributors to process photosets from medium-sized running events. Simulation results confirm, that the use of data fusion in processing the results of crowd computing is a promising technique, and the use of probabilistic graphical models (e.g., Bayesian networks) for data fusion allows one to smoothly increase the quality of the results with an increase of the available information.

MOTOR VEHICLE SAFETY SYSTEM BASED MIKROKONTROLLER ATmega328 USE SMS GATE WAY AND MODUL GPS

&lt;p&gt;&lt;em&gt;Motorcycle theft cases are increasing lately. However, the level of successful case disclosure by the police is classified as minimal. One of the reasons is the difficulty of the investigating team to locate the stolen vehicle. One solution that can be done is to install a tracker on a motorcycle. With the development of technology, we can use GPS devices as a&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;em&gt; tool to track vehicles from theft. The general objective of this research is to reduce the crime rate of motorcycle theft that currently occurs in Indonesia. The specific design objectives are: (1). Facilitate the security apparatus (2). Eliminate public anxiety about motorbike theft cases. For that we need a better security system as a safety for motor vehicles. This tool is equipped with features or services that support services on motorbikes, namely: SMS / SMS Gateway/ SIM800L Module, ATMega328 Microcontroller, GPS (Global Positioning System) Module, and other supporting devices. This tool is connected to an Android device with a GPS tracking system by showing the location of the missing motor vehicle. It is hoped that this security system design can increase the level of security and reduce cases of theft in motor vehicles. This system has also been tested on motorbikes with good results.&lt;/em&gt;&lt;/p&gt;

A pre-processing and network analysis of GPS tracking data

ABSTRACT Global Positioning System (GPS) devices afford the opportunity to collect accurate data on unit movements from temporal and spatial perspectives. With a special focus on GPS technology in travel surveys, this paper proposes: (1) two algorithms for the pre-processing of GPS data in order to deal with outlier identification and missing data imputation; (2) a clustering approach to recover the main points of interest from GPS trajectories; and (3) a weighted-directed network, which incorporates the most relevant characteristics of the GPS trajectories at an aggregate level. A simulation study shows the goodness-of-fit of the imputation data algorithm and the robustness of the clustering algorithm. The proposed algorithms are then applied to three cases studies relating to the mobility of cruise passengers in urban contexts.

GPS TRACKING OF THE FORAGING MOVEMENTS OF OILBIRDS (STEATORNIS CARIPENSIS) FROM THE DUNSTON CAVE AT ASA WRIGHT NATURE CENTRE, TRINIDAD

The ranging behaviors of the Oilbird (Steatornis caripensis) in Trinidad are not well defined and are required to better outline fu- ture conservation management parameters for this species. Due to the nocturnal foraging behaviors of the Oilbird, past observations were restricted to colony site observations, video recordings, and limited electronic tracking over short periods. Utilizing newly developed small- scale Global Positioning System (GPS) data loggers, we were able to determine the flight and foraging behaviors of two oilbirds that roost at the Asa Wright Nature Centre for up to 270 days. The use of backpack style GPS tracking devices provided high site resolution and accuracy of the movements and a better understanding of the annual foraging behaviors of the oilbirds in the Asa Wright Nature Centre colony. The oilbirds’ home range was determined to be 3,564.6 km2, with an average trip distance from Dunston Cave of 7.33 km, excluding an interme- diary migration point in Venezuela. 48.5% of the foraging area consisted of locations within the Northern Range, while 27.7% of these trips remained within the Arima Valley, which is only partially protected from quarrying activities. This data document the previously unknown patterns of habitat use of the Oilbird and serves as a pilot study to determine the conservation importance of those habitats for the develop- ment of long-term conservation action plans for these iconic birds.

Socioeconomic inequalities in children’s exposure to tobacco retailing based on individual-level GPS data in Scotland

BackgroundIdentifying factors shaping knowledge of and attitudes towards tobacco products in preadolescence is a key component supporting tobacco control policies aimed at preventing smoking initiation. This study quantified exposure to...

GPS derived crustal velocity, tectonic deformation and strain in the Indian Himalayan arc

The present study elucidates velocity of the Indian plate from 2013 to 2016 with the help of six continuously operating permanent Global Positioning System (GPS) stations and tracks the crustal motion and direction directly by means of space-based geodetic measurements. The GPS derived velocity estimates were computed with respect to 2008 International Terrestrial Reference Frame (ITRF 08) and carried out almost all along the arc in the Indian Himalayan Region (IHR). The velocity vectors indicate that the Indian plate is moving towards the NE direction (average velocity of 46.95 ± 0.23 mm/yr) with higher movement towards the eastward direction (36.11 ± 0.17 mm/yr) compared to the northward direction (29.02 ± 0.16 mm/yr). Variations in the plate motion between IISC and permanent GPS stations suggest that presently, the convergence rate is about 8.06 ± 0.28 mm/yr and 5.71 ± 0.17 mm/yr for Higher Himalaya and Lesser Himalaya respectively. In addition, the deformation rate was also calculated on the basis of baseline shortening between IGS stations (IISC and LHAZ) and permanent GPS stations. The results suggest that the deformation is about 11.68 ± 1.32 mm/yr above the Main Central Thrust (MCT) and 6.74 ± 1.20 mm/yr above the Main Boundary Thrust (MBT) with respect to the Indian plate (IISC), while it is 20.60 ± 1.76 mm/yr above the MCT and 11.42 ± 1.21 mm/yr above the MBT with respect to Eurasian plate (LHAZ). The strain rate through GPS measurement reveals that the maximum strain is accumulated in the central part of the Himalaya between the MBT and MCT, indicating vulnerability of the central Indian Himalaya for future earthquakes.

Family GPS Tracking for Android

In smart phone field, the security, tracking lost and kidnapping prevention are one of the main areas in current days. Today family members safety is more important when they are outside home. So, there is a need for a tracking system for kids where the parents can monitor their kids at anytime from anywhere. These security goals are achieved by the Android System, Global System for Mobile (GSM), Global Positioning System (GPS) and Short Message Service (SMS) technologies. The proposed application must be installed in all family member's smart phones, and these smart phones will use GPS services without need for internet connection, where the GPS is used to locate the child specifically and to track the child whenever he changes his place. The location obtained by only depending on GSM by sending SMS which contain a link of Google map shows the information of the position. The application also has the ability to trigger a help SMS to his parents when the child is in a dangerous situation by using his headphone, and this is considering a help message in real time.

Microhabitat selection by giant pandas

Understanding habitat selection is important for effective habitat management and recovery of species. However, many habitat selection studies are based on presence and absence data and do not differentiate the intensity of use and its association with fine-scale habitat characteristics. Such information is critical for improving our understanding of habitat suitability to inform conservation planning and practices, particularly for vulnerable species such as the giant panda (Ailuropoda melanoleuca) in China. We integrated Global Positioning Systems (GPS) tracking data of 5 giant pandas in Wolong Nature Reserve, China with detailed vegetation surveys to understand habitat selection by giant pandas. We compared microhabitat characteristics between the core and secondary home range areas of giant pandas and determined their relative importance using a resource selection function (RSF). We found that giant panda core areas had higher elevations, shorter distance to animal paths, shorter trees, and higher density of bamboo than the secondary area. Our findings shed new light on the importance of microhabitat characteristics that are generally overlooked in coarse-scale models in influencing giant panda habitat selection within the home range, such as bamboo density and accessibility to habitat that play important roles in the determination of core areas. We suggest prioritizing dense bamboo forests and areas with animal paths to improve giant pandas' habitat management, restoration, and corridor construction. The methods we used here regarding combining GPS-tracking derived intensity of use data and detailed habitat surveys could also be applied to better understand habitat selection strategies of a variety of other wildlife species.

Associations of spatial mobility with sexual risk behaviors among young men who have sex with men in New York City: A global positioning system (GPS) study.

Spatial contexts and spatial mobility are important factors of the HIV epidemic and sexually transmitted infections. Using global positioning system (GPS) devices, we examined the associations of objectively measured spatial mobility with sexual risk behaviors among gay, bisexual and other men who have sex with men (MSM) in New York City. This observational study included a subgroup of 253 HIV-negative MSM from the Project 18 Cohort Study, who participated in the GPS monitoring sub-study. Spatial mobility was measured as (1) distance traveled and (2) activity space size defined as daily path area during 2-week of GPS tracking. We examined the associations of these measures with numbers of male sexual partners and condomless anal intercourse (CAI) acts during last six months using quasi-Poisson models, adjusting for socio-demographics. Results demonstrated that spatial mobility was positively associated with sexual risk behaviors, for example, with CAI (incidence rate ratio [IRR]=1.01 for a 10km increase in distance traveled and IRR=1.04 for a 1km2 increase in 50m-buffer activity space size). Our findings may enhance the understanding of spatial contexts of HIV risk. Future studies should be conducted to examine the mechanisms for the associations between spatial mobility behaviors with sexual risk behaviors as well as the influence of neighborhood characteristics in various neighborhood contexts, which may guide the place-based HIV prevention services.

Extracting typical occupancy schedules from social media (TOSSM) and its integration with building energy modeling

Building occupancy, one of the most important consequences of occupant behaviors, is a driving influencer for building energy consumption and has been receiving increasing attention in the building energy modeling community. With the vast development of information technologies in the era of the internet-of-things, occupant sensing and data acquisition are not limited to a single node or traditional approaches. The prevalence of social networks provides a myriad of publically available social media data that might contain occupancy information in the space for a given time. In this paper, we explore two approaches to extract the typical occupancy schedules for the input to the building energy simulation based on the data from social networks. The first approach uses text classification algorithms to identify whether people are present in the space where they are posting on social media. On top of that, the typical building occupancy schedules are extracted with assumed people counting rules. The second approach utilizes the processed Global Positioning System (GPS) tracking data provided by social networking service companies such as Facebook and Google Maps. Web scraping techniques are used to obtain and post-process the raw data to extract the typical building occupancy schedules. The results show that the extracted building occupancy schedules from different data sources (Twitter, Facebook, and Google Maps) share a similar trend but are slightly distinct from each other and hence may require further validation and corrections. To further demonstrate the application of the extracted Typical Occupancy Schedules from Social Media (TOSSM), data-driven models for predicting hourly energy usage prediction of a university museum are developed with the integration of TOSSM. The results indicate that the incorporation of TOSSM could improve the hourly energy usage prediction accuracy to a small extent regarding the four adopted evaluation metrics for this museum building.