26 resultados para Positioning System

em Deakin Research Online - Australia


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Global positioning system (GPS) technology has improved the speed, accuracy, and ease of time-motion analyses of field sport athletes. The large volume of numerical data generated by GPS technology is usually summarized by reporting the distance traveled and time spent in various locomotor categories (e.g., walking, jogging, and running). There are a variety of definitions used in the literature to represent these categories, which makes it nearly impossible to compare findings among studies.

The purpose of this work was to propose standard definitions (velocity ranges) that were determined by an objective analysis of time-motion data. In addition, we discuss the limitations of the existing definition of a sprint and present a new definition of sprinting for field sport athletes.

Twenty-five GPS data files collected from 5 different sports (men’s and women’s field hockey, men’s and women’s soccer, and Australian Rules Football) were analyzed to identify the average velocity distribution. A curve fitting process was then used to determine the optimal placement of 4 Gaussian curves representing the typical locomotor categories. 


Based on the findings of these analyses, we make recommendations about sport- specific velocity ranges to be used in future time-motion studies of field sport athletes. We also suggest that a sprint be defined as any movement that reaches or exceeds the sprint threshold velocity for at least 1 second and any movement with an acceleration that occurs within the highest 5% of accelerations found in the corresponding velocity range.

From a practical perspective, these analyses provide conditioning coaches with information on the high-intensity sprinting demands of field sport athletes, while also providing a novel method of capturing maximal effort, short-duration sprints.

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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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 The presence of a wide areal extent of small-sized village reservoirs offers a considerable potential for the development of culture-based fisheries (CBFs) in Sri Lanka. To this end, this study uses geographical information systems (GISs) and remote sensing (RS) techniques to determine the morphometric and biological characteristics most useful for classifying non-perennial reservoirs for CBF development and for assessing the influence of catchment land-use patterns on potential CBF yields. The reservoir shorelines at full water supply level were mapped with a Global Positioning System to determine shoreline length and reservoir areal extent. The ratio of shoreline length to reservoir extent, which was reported to be a powerful predictor variable of CBF yields, could be reliably quantified using RS techniques. The areal extent of reservoirs, quantified with RS techniques (RS extent), was used to estimate the ratio of forest cover plus scrubland cover to RS extent and was found to be significantly related to the CBF yield (R2 = 0.400; P < 0.05). The results of this study indicated that morphometric characteristics and catchment land-use patterns, which might be viewed as indices of biological productivity, can be quantified using RS and GIS techniques. © 2014 Wiley Publishing Asia Pty Ltd.

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Knowledge of the spatial arrangement of the seagrass distribution and biomass within the Hopkins Estuary is an essential step towards gaining an understanding of the functioning of the estuarine ecosystem. This study marks the first attempt to map seagrass distribution and model seagrass biomass and epiphyte biomass along depth gradients by the use of global positioning system (GPS) and geographical information system (GIS) technologies in the estuary. For mapping seagrass in small estuaries, ground-surveying the entire system is feasible. Three species of seagrasses, Heterozostera tasmanica (Martens ex Aschers), Zostera muelleri (Irmisch ex Aschers) and Ruppia megacarpa (Mason), were identified in the Hopkins Estuary. All beds investigated contained a mixed species relationship. Three harvest techniques were trialed in a pilot study, with the 25 × 25-cm quadrat statistically most appropriate. Biomass of seagrasses and epiphytes was found to vary significantly with depth, but not between sites. The average estimate of biomass for total seagrasses and their epiphytes in the estuary in January 2000 was 222.7 g m–2 (dry weight). Of the total biomass, 50.6% or 112.7 g m–2 (dry weight) was contributed by seagrasses and 49.4% of the biomass (110.0 g m–2) were epiphytes. Of the 50.6% of the total biomass represented by seagrasses, 39.3% (87.5 g m–2) were leaves and 11.3% (25.2 g m–2) were rhizomes. The total area of seagrasses present in the Hopkins Estuary was estimated to be 0.4 ± 0.005 km2, with the total area of the estuary estimated to be 1.6 ± 0.02 km2 (25% cover). The total standing crop of seagrasses and epiphytes in the Hopkins Estuary in January 2000 was estimated to be 102.3 ± 57 t in dry weight, 56% (56.9 ± 17 t, dry weight) seagrasses and 44% (45.4 ± 19 t, dry weight) epiphytes. Of the seagrass biomass, 39% (39.7 ± 13 t, dry weight) was contributed by leaves and 17% (17.3 ± 7 t, dry weight) by rhizomes.

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Mobility has become a key factor around the world, as the use of ubiquitous devices, including laptops, personal digital assistants (PDAs), and mobile phones, are increasingly becoming part of daily life (Steinfield, 2004). Adding mobility to computing power, and with advanced personalization of technologies, new business applications are emerging in the area of mobile communications (Jagoe, 2003). The fastest growing segment among these applications is location-based services. This article offers a brief overview of services and their supporting technologies, and provides an outlook for their future.

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In recent years there has been an increase in community-based monitoring programmes developed and implemented worldwide. This paper describes how the data collected from such a programme could be integrated into a Geographic Information System (GIS) to create temperate subtidal marine habitat maps. A differential Global Positioning System was utilized to accurately record the location of the trained community-based SCUBA diver data. These georeferenced data sets were then used to classify benthic habitats using an aerial photograph and digitizing techniques. This study demonstrated that trained community-based volunteers can collect data that can be utilized within a GIS to create reliable and cost-effective maps of shallow temperate subtidal rocky reef systems.

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Submarine Sonar FX is a sound effect of a submarine/nautical type of sonar.

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This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing state-of-the-art probabilistic clustering technique, the Latent Dirichiet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.

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Abstract. Both physical and social environmental factors influence young children’s physical activity, yet little is known about where Hispanic children are more likely to be active. We assessed the feasibility of simultaneously measuring, then processing objective measures of location and physical activity among Hispanic preschool children. Preschool-aged Hispanic children (n = 15) simultaneously wore QStarz BT100X global positioning system (GPS) data loggers and Actigraph GT3X accelerometers for a 24- to 36-hour period, during which time their parents completed a location and travel diary. Data were aggregated to the minute and processed using the personal activity location measurement system (PALMS). Children successfully wore the GPS data loggers and accelerometers simultaneously, 12 of which yielded data that met quality standards. The average percent correspondence between GPS- and diary-based estimates of types of location was high and Kappa statistics were moderate to excellent, ranging from 0.49-0.99. The between method (GPS monitor, parent-reported diary) correlations of estimated participant-aggregated minutes spent on vehicle-based trips were strong. The simultaneous use of GPS and accelerometers to assess Hispanic preschool children’s location and physical activity is feasible. This methodology has the potential to provide more precise findings to inform environmental interventions and policy changes to promote physical activity among Hispanic preschool children.

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In recent years, marine predator and seabird tracking studies have become ever more popular. However, they are often conducted without first considering how many individuals should be tracked and for how long they should be tracked in order to make reliable predictions of a population's home-range area. Home-range area analysis of two seabird-tracking data sets was used to define the area of active use (where birds spent 100% of their time) and the core foraging area (where birds spent 50% of their time). Analysis was conducted on the first foraging trip undertaken by the birds and then the first two, three and four foraging trips combined. Appropriate asymptotic models were applied to the data, and the calculated home-range areas were plotted as a function of an increasing number of individuals and trips included in the sample. Data were extrapolated from these models to predict the area of active use and the core foraging area of the colonies sampled. Significant variability was found in the home-range area predictions made by analysis of the first foraging trip and the first four foraging trips combined. For shags, the first foraging trip predicted a 56% smaller area of active use when compared to the predictions made by combining the first four foraging trips. For kittiwakes, a 43% smaller area was predicted when comparing the first foraging trip with the four combined trips. The number of individuals that would be required to predict the home range area of the colony depends greatly on the number of trips included in the analysis. This analysis predicted that 39 (confidence interval 29-73) shags and 83 (CI: 109-161) kittiwakes would be required to predict 95% of the area of active use when the first four foraging trips are included in the sample compared with 135 (CI 96-156) shags and 248 (164-484) kittiwakes when only the first trip is included in the analysis. Synthesis and applications. Seabird and marine mammal tracking studies are increasingly being used to aid the designation of marine conservation zones and to predict important foraging areas. We suggest that many studies may be underestimating the size of these foraging areas and that better estimates could be made by considering both the duration and number of data logger deployments. Researchers intending to draw conclusions from tracking data should conduct a similar analysis of their data as used in this study to determine the reliability of their home-range area predictions.

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Purpose : This study was conducted to devise a new individual calibration method to enhance MTI accelerometer estimation of free-living level walking speed.

Method : Five female and five male middle-aged adults walked 400 m at 3.5, 4.5, and 5.5 km·h-1, and 800 m at 6.5 km·h-1 on an outdoor track, following a continuous protocol. Lap speed was controlled by a global positioning system (GPS) monitor. MTI counts-to-speed calibration equations were derived for each trial, for each subject for four such trials with each of four MTI, for each subject for the average MTI, and for the pooled data. Standard errors of the estimate (SEE) with and without individual calibration were compared. To assess accuracy of prediction of free-living walking speed, subjects also completed a self-paced, "brisk" 3-km walk wearing one of the four MTI, and differences between actual and predicted walking speed with and without individual calibration were examined.

Results : Correlations between MTI counts and walking speed were 0.90 without individual calibration, 0.98 with individual calibration for the average MTI, and 0.99 with individual calibration for a specific MTI. The SEE (mean ± SD) was 0.58 ± 0.30 km·h-1 without individual calibration, 0.19 ± 0.09 km·h-1 with individual calibration for the average MTI monitor, and 0.16 ± 0.08 km·h-1 with individual calibration for a specific MTI monitor. The difference between actual and predicted walking speed on the "brisk" 3-km walk was 0.06 ± 0.25 km·h-1 using individual calibration and 0.28 ± 0.63 km·h-1 without individual calibration (for specific accelerometers).

Conclusion : MTI accuracy in predicting walking speed without individual calibration might be sufficient for population-based studies but not for intervention trials. This individual calibration method will substantially increase precision of walking speed predicted from MTI counts.

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1Reproductive fitness is often compromised at the margins of a species’ range due to sub-optimal conditions.2Set against this backdrop, the Mediterranean's largest loggerhead sea turtle (Caretta caretta) rookery at Zakynthos (Greece) presents a conundrum, being at a very high latitude for this species, yet hosting a high concentration of nesting.3We used visual surveys combined with global positioning system (GPS) tracking to show that at the start of the breeding season, individuals showed microhabitat selection, with females residing in transient patches of warm water. As the sea warmed in the summer, this selection was no longer evident.4As loggerhead turtles are ectothermic, this early season warm-water selection presumably speeds up egg maturation rates before oviposition, thereby allowing more clutches to be incubated when sand conditions are optimal during the summer.5Active selection of warm waters may allow turtles to initiate nesting at an earlier date.

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Recent advances in telemetry technology have created a wealth of tracking data available for many animal species moving over spatial scales from tens of meters to tens of thousands of kilometers. Increasingly, such data sets are being used for quantitative movement analyses aimed at extracting fundamental biological signals such as optimal searching behavior and scale-dependent foraging decisions. We show here that the location error inherent in various tracking technologies reduces the ability to detect patterns of behavior within movements. Our analyses endeavored to set out a series of initial ground rules for ecologists to help ensure that sampling noise is not misinterpreted as a real biological signal. We simulated animal movement tracks using specialized random walks known as Lévy flights at three spatial scales of investigation: 100-km, 10-km, and 1-km maximum daily step lengths. The locations generated in the simulations were then blurred using known error distributions associated with commonly applied tracking methods: the Global Positioning System (GPS), Argos polar-orbiting satellites, and light-level geolocation. Deviations from the idealized Lévy flight pattern were assessed for each track after incrementing levels of location error were applied at each spatial scale, with additional assessments of the effect of error on scale-dependent movement patterns measured using fractal mean dimension and first-passage time (FPT) analyses. The accuracy of parameter estimation (Lévy μ, fractal mean D, and variance in FPT) declined precipitously at threshold errors relative to each spatial scale. At 100-km maximum daily step lengths, error standard deviations of ≥10 km seriously eroded the biological patterns evident in the simulated tracks, with analogous thresholds at the 10-km and 1-km scales (error SD ≥ 1.3 km and 0.07 km, respectively). Temporal subsampling of the simulated tracks maintained some elements of the biological signals depending on error level and spatial scale. Failure to account for large errors relative to the scale of movement can produce substantial biases in the interpretation of movement patterns. This study provides researchers with a framework for understanding the limitations of their data and identifies how temporal subsampling can help to reduce the influence of spatial error on their conclusions.

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This study presents an analysis of the application of underwater video data collected for training and validating benthic habitat distribution models. Specifically, we quantify the two major sources of error pertaining to collection of this type of reference data. A theoretical spatial error budget is developed for a positioning system used to co-register video frames to their corresponding locations at the seafloor. Second, we compare interpretation variability among trained operators assessing the same video frames between times over three hierarchical levels of a benthic classification scheme. Propagated error in the positioning system described was found to be highly correlated with depth of operation and varies from 1.5m near the surface to 5.7m in 100m of water. In order of decreasing classification hierarchy, mean overall observer agreement was found to be 98% (range 6%), 82% (range 12%) and 75% (range 17%) for the 2, 4, and 6 class levels of the scheme, respectively. Patterns in between-observer variation are related to the level of detail imposed by each hierarchical level of the classification scheme, the feature of interest, and to the amount of observer experience. © 2014 Copyright © Taylor & Francis Group, LLC.

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 Microsoft Kinect sensor was introduced with the XBOX gaming console. It features a simple and portable motion capturing system. Kinect nowadays presents a point of interest in many fields of study and areas of research where its affordable price compared to its capabilities. The Kinect sensor has the capability to capture and track detected 3D objects with accuracy comparable to that captured by state of the art commercial systems. Human safety is considered one of the highest concerns, specially nowadays where the existence of machines and robots is widely used. In this paper we present using the Kinect technology for enhancing the safety of equipment and operations in seven different applications. These applications include 1) positioning of child’s car seat to optimise the child’s position in respected to front and side air-bags; 2) board positioning system to improve the teacher’s arm reach posture; 3) gas station safety to prevent children from accessing the gas pump; 4) indoor pool safety to avoid children access to deep pool area; 5) robot safety emergency stop; 6) Workplace safety; and 7) older adults fall prediction.