Quantifying time spent outdoors: A versatile method using any type of global positioning system (GPS) and accelerometer devices

Spending time outdoors is associated with increased time spent in physical activity, lower chronic disease risk, and wellbeing. Many studies rely on self-reported measures, which are prone to recall bias. Other methods rely on features and functions only available in some GPS devices. Thus, a reliable and versatile method to objectively quantify time spent outdoors is needed. This study sought to develop a versatile method to classify indoor and outdoor (I/O) GPS data that can be widely applied using most types of GPS and accelerometer devices. To develop and test the method, five university students wore an accelerometer (ActiGraph wGT3X-BT) and a GPS device (Canmore GT-730FL-S) on an elastic belt at the right hip for two hours in June 2022 and logged their activity mode, setting, and start time via activity diaries. GPS trackers were set to collect data every 5 seconds. A rule-based point cluster-based method was developed to identify indoor, outdoor, and in-vehicle time. Point clusters were detected using an application called GPSAS_Destinations and classification were done in R using accelerometer lux, building footprint, and park location data. Classification results were compared with the submitted activity diaries for validation. A total of 7,006 points for all participants were used for I/O classification analyses. The overall I/O GPS classification accuracy rate was 89.58% (Kappa = 0.78), indicating good classification accuracy. This method provides reliable I/O clarification results and can be widely applied using most types of GPS and accelerometer devices.

How to improve automated external defibrillator placement for out-of-hospital cardiac arrests: A case study

Introduction

In out-of-hospital cardiac arrests (OHCAs), the use of an automatic external defibrillator (AED) by a bystander remains low, as AEDs may be misplaced with respect to the locations of OHCAs. As the distribution of historical OHCAs is potentially predictive of future OHCA locations, the purpose of this study is to assess AED positioning with regard to past locations of OHCAs, in order to improve the efficiency of public access defibrillation programs.

Methods

This is a retrospective observational study from 2014 to 2018. The locations of historical OHCAs and AEDs were loaded into a geodata processing tool. Median distances between AEDs were collected, as well as the number and rates of OHCAs covered (distance of <100 meters from the nearest AED). Areas with high densities of uncovered OHCAs (hotspots) were identified in order to propose the placement of additional AEDs. Areas over-covered by AEDs (overlays) were also identified in order to propose the relocation of overlapping AEDs.

Results

There were 2,971 OHCA, 79.3% of which occurred at home, and 633 AEDs included in the study. The global coverage rate was 7.5%. OHCAs occurring at home had a coverage rate of 4.5%. Forty hotspots were identified, requiring the same number of additional AEDs. The addition of these would increase the coverage from 7.5% to 17.6%. Regarding AED overlays, 17 AEDs were found to be relocatable without reducing the AED coverage of historical OHCAs.

Discussion

This study confirms that geodata tools can assess AED locations and increase the efficiency of their placement. Historical hotspots and AED overlays should be considered, with the aim of efficiently relocating or adding AEDs. At-home OHCAs should become a priority target for future public access defibrillation programs as they represent the majority of OHCAs but have the lowest AED coverage rates.

An improved method using adaptive smoothing for GNSS tomographic imaging of ionosphere

Global navigation satellite system (GNSS) is a well-established sensors in the recent ionosphere research. By comparing with classical meteorological equipments, the GNSS application can obtain more reliable and precious ionospheric total electron content (TEC) result. However, the most used GNSS ionospheric tomography technique is sensitive to a priori information due to the sparse and non-uniform distribution of GNSS stations. In this paper, we propose an improved method based on adaptive Laplacian smoothing and algebraic reconstruction technique (ALS-ART). Compared with traditional constant constraints, this method is less dependent on a priori information and adaptive smoothing constraints is closer to the actual situation. Tomography experiments using simulated data show that reconstruction accuracy of ionospheric electron density using ALS-ART method is significantly improved. We also use the method to do the analysis of real observation data and compare the tomography results with ionosonde observation data. The results demonstrate the superiority and reliability of the proposed method compared to traditional constant constraints method which will further improve the capability of obtaining precious ionosphere TEC by using GNSS.

The estimation of non-irrigated crop area and production using the regression analysis approach: A case study of Bursa Region (Turkey) in the mid-nineteenth century

Agricultural land cover and its changing extent are directly related to human activities, which have an adverse impact on the environment and ecosystems. The historical knowledge of crop production and its cultivation area is a key element. Such data provide a base for monitoring and mapping spatio-temporal changes in agricultural land cover/use, which is of great significance to examine its impacts on environmental systems. Historical maps and related data obtained from historical archives can be effectively used for reconstruction purposes through using sample data from ground observations, government inventories, or other historical sources. This study considered historical population and cropland survey data obtained from Ottoman Archives and cropland suitability map, accessibility, and geophysical attributes as ancillary data to estimate non-irrigated crop production and its corresponding cultivation area in the 1840s Bursa Region, Turkey. We used the regression analysis approach to estimate agricultural land area and grain production for the unknown data points in the study region. We provide the spatial distribution of production and its cultivation area based on the estimates of regression models. The reconstruction can be used in line with future historical research aiming to model landscape, climate, and ecosystems to assess the impact of human activities on the environmental systems in preindustrial times in the Bursa Region context.

Bayesian hierarchical spatial count modeling of taxi speeding events based on GPS trajectory data

Speeding behavior, especially serious speeding, is more common in taxi driver than other driving population due to their high exposure under traffic environment, which increases the risk of being involved in crashes. In order to prevent the taxi and other road users from speed-related crash, previous studies have revealed contributors of demographic and driving operation affecting taxi speeding frequency. However, researches regarding road factors, and spatial effect are typically rare. For this sake, the current study explores the contributions of 10 types of road characteristics and two kinds of spatial effects (spatial correlation and spatial heterogeneity) on taxi total speeding and serious speeding frequency. Taxi GPS trajectory data in a Chinese metropolis were used to identify speeding event. The study then established four kinds of Bayesian hierarchical count models base on Poisson and negative binominal distribution to estimate the contributor impacts, respectively. Results show that Bayesian hierarchical spatial Poisson log-linear model is optimum for fitting both total and serious speeding frequency. For the analysis, it is found that drivers are more likely to commit speeding on long multilane road with median strip, and road with non-motorized vehicle lane, bus-only lane and viaduct or road tunnel. Roads with low speed limit, and work zone are associated with increasing speeding as well. In terms of serious speeding, bus-only lane is not a contributor, while road speed camera number and one-way organization are significantly positive to the speeding frequency. Furthermore, it reveals that two spatial effects significantly increase the occurrence of speeding events; the impact of spatial heterogeneity is more critical.

Landscape Genetics of Plants: Challenges and Opportunities

Dispersal is one of the most important but least understood processes in plant ecology and evolutionary biology. Dispersal of seeds maintains and establishes populations, and pollen and seed dispersal are responsible for gene flow within and among populations. Traditional views of dispersal and gene flow assume models that are governed solely by geographic distance and do not account for variation in dispersal vector behavior in response to heterogenous landscapes. Landscape genetics integrates population genetics with Geographic Information Systems (GIS) to evaluate the effects of landscape features on gene flow patterns (effective dispersal). Surprisingly, relatively few landscape genetic studies have been conducted on plants. Plants present advantages because their populations are stationary, allowing more reliable estimates of the effects of landscape features on effective dispersal rates. On the other hand, plant dispersal is intrinsically complex because it depends on the habitat preferences of the plant and its pollen and seed dispersal vectors. We discuss strategies to assess the separate contributions of pollen and seed movement to effective dispersal and to delineate the effects of plant habitat quality from those of landscape features that affect vector behavior. Preliminary analyses of seed dispersal for three species indicate that isolation by landscape resistance is a better predictor of the rates and patterns of dispersal than geographic distance. Rates of effective dispersal are lower in areas of high plant habitat quality, which may be due to the effects of the shape of the dispersal kernel or to movement behaviors of biotic vectors. Landscape genetic studies in plants have the potential to provide novel insights into the process of gene flow among populations and to improve our understanding of the behavior of biotic and abiotic dispersal vectors in response to heterogeneous landscapes.

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops

Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R² = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.

Sensitivity, reliability and construct validity of GPS and accelerometers for quantifying peak periods of rugby competition

Training prescription and monitoring of team-sport athletes rely on accurate quantification of player movement. Our aim was to determine the sensitivity, reliability and construct validity of measures derived from a wearable device incorporating Global Positioning System (GPS) and accelerometer technology to quantify the peak periods of rugby competition. Match movement data were collected from 30 elite and 30 sub-elite rugby union players across respective competitive seasons. Accelerometer and GPS measures were analysed using a rolling average to identify peak movement for epochs ranging from 5 to 600 seconds. General linear mixed modelling was used to quantify the effects of playing position and match-half on the peak movement and variabilities within and between players represented reliability of each measure. Mean positional differences and match-half changes were assessed via standardisation and magnitude-based decisions. Sensitivity of measures was quantified via evaluation of ("signal") and typical error of measurement ("noise"). GPS and accelerometer measures had poor sensitivity for quantifying peak movement across all epochs and both levels of rugby union competition (noise 4× to 5× the signal). All measures displayed correspondingly low reliability across most epochs and both levels of competition (ICC<0.50). Construct validity was evident in mean differences between playing positions and match halves that were consistent with expected activity profiles in rugby union. However, it was clear from the pattern of differences across epoch durations and levels of competition that GPS and accelerometer measures provided different information about player movement. The poor sensitivity and low reliability of GPS and accelerometer measures of peak movement imply that rugby union players need to be monitored across many matches to obtain adequate precision for assessing individuals. Although all measures displayed construct validity, accelerometers provided meaningful information additional to that of GPS. We recommend using accelerometers alongside GPS to monitor and prescribe match respresentative training.

Are safety and performance affected by navigation system display size, environmental illumination, and gender when driving in both urban and rural areas?

Driving with the aid of a navigation system could distract drivers. A high level of distraction influences driver performance and safety, leading to a possible increase in road crashes. The illumination level and size of the GPS display may influence the duration and frequency of a driver's glances, which in turn may affect driver distraction. In a simulated driving experiment requiring the use of a GPS, the GPS's display size and illumination level were examined, in light of the driver's experience and gender, to understand their effects on the performance and safety of young drivers on roads in urban and rural areas. Twenty young subjects, male and female between the ages of 18 and 29 years, participated in this experiment. Driving safety was evaluated by lateral control (number of pavement and lane line crossings), number of crashes, number of near misses, and the total time out of the lane. Driving performance was evaluated by the number of navigational errors, the total time making navigational errors, number of times the speed limit was exceeded and total amount of time speeding. These measures were analyzed using a repeated measures analysis of variance (ANOVA) model. Furthermore, the effects of the GPS display, considering the driving experience, were investigated with a simple linear regression. Findings suggest that driving with a small GPS display in an urban area leads to more navigational errors than driving with a large GPS display. Furthermore, more speed limit violations tend to occur in rural areas in the daytime than at night. Moreover, in urban areas, male drivers tend to have the highest number of crashes during the daytime. Furthermore, in rural areas, males tend to violate the speed limit more often and for longer periods of time during the daytime than at night and more than females do. Additionally, when navigating with a GPS system, young experienced drivers drive safer than inexperience drivers. The findings are of interest to designers and transportation researchers concerned with improving GPSs to enhance driving safety and performance.

The applicability of recreation-grade GNSS receiver (GPS watch, Suunto Ambit Peak 3) in a forested and an open area compared to a mapping-grade receiver (Trimble Juno T41)

Due to developments in global navigation satellite systems (GNSS) and the miniaturization of their components, the usage of Global Positioning System (GPS) is no longer restricted to professional applications, but has become available in various consumer type devices, such as wristwatches. These commercial devices, however, were primarily designed for tracking activities in predominately urban settings and their accuracy has not been tested in forested areas. In this study, we present an assessment of the positional accuracy of a GPS watch (Ambit Peak 3, Suunto, Finland) under different forest cover types, seasons and meteorological conditions within the Whitehall Forest GPS Test Site located in Athens, Georgia, USA. As a standard of comparison, the performance of the GPS watch measurements was juxtaposed to that of a mapping-grade receiver (Juno T41, Trimble Inc., USA). In this study, we analyzed the differences between the determined and control positions using root-mean-square-error (RMSE), along with the distribution of observed positions through the standard deviational ellipse. The results suggest that the seasonal variations contributed to a statistically significant impact on the RMSE values for the GPS watch. However, there were no statistically significant differences in horizontal position accuracy by forest cover-type when using the GPS watch. Furthermore, no significant differences were found in horizontal position accuracy during the leaf-off period between the RMSE values for the GPS watch and those of the mapping-grade receiver. Lastly, the positional accuracies for both types of receivers were found to be weakly, but significantly correlated with fluctuations in air temperature and absolute humidity.

A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.

Football-specific validity of TRACAB's optical video tracking systems

The present study aimed to validate and compare the football-specific measurement accuracy of two optical tracking systems engineered by TRACAB. The “Gen4” system consists of two multi-camera units (a stereo pair) in two locations either side of the halfway line, whereas the distributed “Gen5” system combines two stereo pairs on each side of the field as well as two monocular systems behind the goal areas. Data were collected from 20 male football players in two different exercises (a football sport-specific running course and small-sided games) in a professional football stadium. For evaluating the accuracy of the systems, measures were compared against simultaneously recorded measures of a reference system (VICON motion capture system). Statistical analysis uses RMSE for kinematic variables (position, speed and acceleration) and the difference in percentages for performance indicators (e.g. distance covered, peak speed) per run compared to the reference system. Frames in which players were obviously not tracked were excluded. Gen5 had marginally better accuracy (0.08 m RMSE) for position measurements than Gen4 (0.09 m RMSE) compared to the reference. Accuracy difference in instantaneous speed (Gen4: 0.09 m⋅s^-1 RMSE; Gen5: 0.08 m⋅s^-1 RMSE) and acceleration (Gen4: 0.26 m⋅s^-2 RMSE; Gen5: 0.21 m⋅s^-2 RMSE) measurements were significant, but also trivial in terms of the effect size. For total distance travelled, both Gen4 (0.42 ± 0.60%) and Gen5 (0.27 ± 0.35%) showed only trivial deviations compared to the reference. Gen4 showed moderate differences in the low-speed distance travelled category (-19.41 ± 13.24%) and small differences in the high-speed distance travelled category (8.94 ± 9.49%). Differences in peak speed, acceleration and deceleration were trivial (<0.5%) for both Gen4 and Gen5. These findings suggest that Gen5’s distributed camera architecture has minor benefits over Gen4’s single-view camera architecture in terms of accuracy. We assume that the main benefit of the Gen5 towards Gen4 lies in increased robustness of the tracking when it comes to optical overlapping of players. Since differences towards the reference system were very low, both TRACAB’s tracking systems can be considered as valid technologies for football-specific performance analyses in the settings tested as long as players are tracked correctly.

Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative, which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekend/weekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data’s contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.

Drop-And-Spin Virtual Neighborhood Auditing: Assessing Built Environment for Linkage to Health Studies

INTRODUCTION

Various built environment factors might influence certain health behaviors and outcomes. Reliable, resource-efficient methods that are feasible for assessing built environment characteristics across large geographies are needed for larger, more robust studies. This paper reports the item response prevalence, reliability, and rating time of a new virtual neighborhood audit protocol, drop-and-spin auditing, developed for assessment of walkability and physical disorder characteristics across large geographic areas.

METHODS

Drop-and-spin auditing, a method where a Google Street View scene was rated by spinning 360° around a point location, was developed using a modified version of the virtual audit tool Computer Assisted Neighborhood Visual Assessment System. Approximately 8,000 locations within Essex County, New Jersey were assessed by 11 trained auditors. Using a standardized protocol, 32 built environment items per a location within Google Street View were audited. Test-retest and inter-rater κ statistics were from a 5% subsample of locations. Data were collected in 2017-2018 and analyzed in 2018.

RESULTS

Roughly 70% of Google Street View scenes had sidewalks. Among those, two thirds were in good condition. At least 5 obvious items of garbage or litter were present in 41% of Google Street View scenes. Maximum test-retest reliability indicated substantial agreement (κ ≥0.61) for all items. Inter-rater reliability of each item, generally, was lower than test-retest reliability. The median time to rate each item was 7.3 seconds.

CONCLUSIONS

Compared with segment-based protocols, drop-and-spin virtual neighborhood auditing is quicker and similarly reliable for assessing built environment characteristics. Assessment of large geographies may be more feasible using drop-and-spin virtual auditing.

Choropleth map legend design for visualizing the most influential areas in article citation disparities: A bibliometric study

BACKGROUND

Disparities in health outcomes across countries/areas are a central concern in public health and epidemiology. However, few authors have discussed legends that can be complemental to choropleth maps (CMs) and merely linked differences in outcomes to other factors like density in areas. Thus, whether health outcome rates on CMs showing the geographical distribution can be applied to publication citations in bibliometric analyses requires further study. The legends for visualizing the most influential areas in article citation disparities should have sophisticated designs. This paper illustrates the use of cumulative frequency (CF) map legends along with Lorenz curves and Gini coefficients (GC) to characterize the disparity of article citations in areas on CMs, based on the quantile classification method for classes.

METHODS

By searching the PubMed database (pubmed.com), we used the keyword "Medicine" [journal] and downloaded 7042 articles published from 1945 to 2016. A total number of 41,628 articles were cited in Pubmed Central (PMC). The publication outputs based on the author's x-index were applied to plot CM about research contributions. The approach uses two methods (i.e., quantiles and equal total values for each class) with CF legends, in order to highlight the difference in x-indices across geographical areas on CMs. GC was applied to observe the x-index disparities in areas. Microsoft Excel Visual Basic for Application (VBA) was used for creating the CMs.

RESULTS

Results showed that the most productive and cited countries in Medicine (Baltimore) were China and the US. The most-cited states and cities were Maryland (the US) and Beijing (China). Taiwan (x-index = 24.38) ranked behind Maryland (25.97), but ahead of Beijing (16.9). China earned lower disparity (0.42) than the US (0.49) and the rest of the world (0.53) when the GCs were applied.

CONCLUSION

CF legends, particularly using the quantile classification for classes, can be useful to complement CMs. They also contain more information than those in standard CM legends that are commonly used with other classification methods. The steps of creating CM legends are described and introduced. Bibliometric analysts on CM can be replicated in the future.

Inferring Micro-Activities Using Wearable Sensing for ADL Recognition of Home-Care Patients

In this study, we propose a novel, context-based, location-aware algorithm for identifying low-level micro-activities that can be used to derive complex activities of daily living (ADL) performed by home-care patients. This identification is achieved by gathering the location information of the target user by using a wearable beacon embedded with a magnetometer and inertial sensors. The shortcomings of beacon-signal stability and mismatch issues in magnetic-field sequences are overcome by adopting a hybrid, three-phase approach for deducing the locus of micro-activities and their associated zones in a smart home environment. The suggested approach is assessed in two different test environments, where the main intention is to map the location of a person performing an activity with pre-defined house landmarks and zones in the offline labeled database. In addition to the recognition of low-level activities, the proposed method also identifies the person's walking trajectory within the same zone or between different zones of the house. The experimental results demonstrate that it is possible to achieve centimeter-level accuracy for the recognition of micro-activities and to achieve the classification accuracy of 85% for trajectory prediction. These results are encouraging and imply that the collection of accurate low-level information for ADL recognition is possible using integration of inertial sensors, magnetic field and Bluetooth low energy (BLE) technologies from the wearable beacon without relying on other infrastructural sensors.

A Google Earth-based database management for schistosomiasis control in Zanzibar

Schistosomiasis remains a serious health problem in Africa. Although a strong, coordinated agenda for research on this disease has been in place for the last 50 years in Zanzibar, data storage, retrieval of survey data and management remain problem areas. We investigated the use of Google Earth (GE) in conjunction with a hand-held, global positioning system as a pilot project for managing schistosomiasis control. In this way, risk areas can be surveyed and followed up by visualizing both the distribution of human infections and that of the intermediate snail host together with environmental information. A platform with three spatial databases was created: i) Distribution of infected humans; ii) Distribution of the intermediate snail host in ponds (infected and not infected specimens); iii) Distribution of the intermediate snail host in streams (infected and non-infected specimens). The GE spatial database increased the efficiency of follow-up case treatment as well as snail control and contributed also to the discovery of previously unknown areas in need of snail control. We conclude that this platform is advantageous not only by being useful for management and visualization of spatial data, but also because it is easy to operate and available free of charge.

Japan Aerospace Exploration Agency's public-health monitoring and analysis platform: A satellite-derived environmental information system supporting epidemiological study

Since the 1970s, Earth-observing satellites collect increasingly detailed environmental information on land cover, meteorological conditions, environmental variables and air pollutants. This information spans the entire globe and its acquisition plays an important role in epidemiological analysis when in situ data are unavailable or spatially and/or temporally sparse. In this paper, we present the development of Japan Aerospace Exploration Agency's (JAXA) Public-health Monitoring and Analysis Platform available from JAXA, a user-friendly, web-based system providing environmental data on shortwave radiation, rainfall, soil moisture, the normalized difference vegetation index, aerosol optical thickness, land surface temperature and altitude. This system has been designed so that users should be able to download and utilize data without the need for additional data processing. The website allows interactive exchange and users can request data for a specific geographic location and time using the information gained for epidemiological analysis.

GPS Laparoscopic Ultrasound: Embedding an Electromagnetic Sensor in a Laparoscopic Ultrasound Transducer

Tracking the location and orientation of a laparoscopic ultrasound (LUS) transducer is a prerequisite in many surgical visualization and navigation applications. Electromagnetic (EM) tracking is a preferred method to track an LUS transducer with an articulating imaging tip. The conventional approach to integrating EM tracking with LUS is to attach an EM sensor on the outer surface of the imaging tip (external setup), which is not ideal for routine clinical use. In this work, we embedded an EM sensor inside a standard LUS transducer. We found that ultrasound image quality and the four-way articulation function of the transducer were not affected by this sensor integration. Furthermore, we found that the tracking accuracy of our integrated transducer was comparable to that of the external setup. An animal study conducted using the developed transducer suggests that an internally embedded EM sensor is a clinically more viable approach, and may be the future of tracking an articulating LUS transducer.

Assessing climate change associated sea-level rise impacts on sea turtle nesting beaches using drones, photogrammetry and a novel GPS system

Climate change associated sea-level rise (SLR) is expected to have profound impacts on coastal areas, affecting many species, including sea turtles which depend on these habitats for egg incubation. Being able to accurately model beach topography using digital terrain models (DTMs) is therefore crucial to project SLR impacts and develop effective conservation strategies. Traditional survey methods are typically low-cost with low accuracy or high-cost with high accuracy. We present a novel combination of drone-based photogrammetry and a low-cost and portable real-time kinematic (RTK) GPS to create DTMs which are highly accurate (<10 cm error) and visually realistic. This methodology is ideal for surveying coastal sites, can be broadly applied to other species and habitats, and is a relevant tool in supporting the development of Specially Protected Areas. Here, we applied this method as a case-study to project three SLR scenarios (0.48, 0.63 and 1.20 m) and assess the future vulnerability and viability of a key nesting habitat for sympatric loggerhead (Caretta caretta) and green turtle (Chelonia mydas) at a key rookery in the Mediterranean. We combined the DTM with 5 years of nest survey data describing location and clutch depth, to identify (a) regions with highest nest densities, (b) nest elevation by species and beach, and (c) estimated proportion of nests inundated under each SLR scenario. On average, green turtles nested at higher elevations than loggerheads (1.8 m vs. 1.32 m, respectively). However, because green turtles dig deeper nests than loggerheads (0.76 m vs. 0.50 m, respectively), these were at similar risk of inundation. For a SLR of 1.2 m, we estimated a loss of 67.3% for loggerhead turtle nests and 59.1% for green turtle nests. Existing natural and artificial barriers may affect the ability of these nesting habitats to remain suitable for nesting through beach migration.

Geolocator deployment reduces return rate, alters selection, and impacts demography in a small songbird

In the past few years, miniature light-level geolocators have been developed for tracking wild bird species that were previously too small to track during their full annual cycle. Geolocators offer an exciting opportunity to study the full annual cycle for many species. However, the potential detrimental effects of carrying geolocators are still poorly understood, especially for small-bodied birds. Here, we deployed light-level geolocators on common yellowthroat warblers (Geothlypis trichas). Over two years, we monitored return rates and neighborhood demography for 40 warblers carrying a geolocator and 20 reference birds that did not carry a geolocator. We compared the two groups with long-term data from 108 unmanipulated birds breeding at the same location in previous and subsequent years. Overall, we found that individuals carrying a geolocator were less likely to return to the study site in the following year (21% to 33% returned, depending on inclusion criteria) than either contemporaneous controls (55%) or long-term controls (55%). Among birds marked with geolocators, we also detected viability selection for greater wing length, whereas this pattern was not present in control birds. Finally, in each year after geolocator deployment, inexperienced breeders colonized vacant territories and this demographic effect persisted for two years after deployment. Sexual selection and ornamentation are strongly age-dependent in this system, and behavioral data collected after geolocator deployment is likely to differ systematically from natural conditions. Clearly geolocators will continue to be useful tools, but we suggest that future studies should carefully consider the potential for biased returns and the ecological validity of behavioral data collected from geolocator marked populations.

Performance of GPS units for deployment on semiaquatic animals

Global Positioning System (GPS) technology is widely used in wildlife research to study animal movement and habitat use. In order to evaluate the quality and reliability of GPS data, the factors influencing the performance of these devices must be known, especially for semiaquatic species, because terrestrial and aquatic habitat might affect GPS performance differently. We evaluated the location error and fix success rate of three GPS receiver models in stationary tests and on a semi-aquatic mammal, the Eurasian beaver (Castor fiber). The location error during stationary tests was on average 15.7 m, and increased with increasing canopy closure, slope, and horizontal dilution of precision, potentially leading to the erroneous classification of GPS positions when studying habitat use in animals. In addition, the position of the GPS antenna (flat versus 90° tilted) affected the location error, suggesting that animal behavior affects GPS performance. The fix success rate was significantly higher during stationary tests compared to when GPS units were deployed on beavers (94% versus 86%). Further, GPS receivers did not obtain any positions underwater and underground, the latter potentially allowing the estimation of activity periods in animals that use lodges or burrows as shelter. We discuss the possibilities for data screening, the use of buffer zones along the shoreline, and combination with other data loggers to avoid the erroneous classification of GPS positions when studying habitat use.

Wearable sensors for multifactorial personal exposure measurements - A ranking study

Individuals are simultaneously exposed to multiple environmental stressors during their daily life. Studies of adverse health effects and their etiology as well as recommendations for a healthier life style demand for an assessment of multifactorial personal exposure, according to the exposome concept. A challenge is to record exposure while people are moving in heterogeneous urban environments. Therefore wearable sensor technologies are becoming a promising way to measure personal exposure continuously: indoors, outdoors and even on the move. So far, studies which test the accuracy and usability of wearable sensors for multiple stressors are lacking. Performance evaluations are important and should take place beforehand, especially to ensure the success of citizens-oriented studies. For the first time we rigorously examined the accuracy and application suitability of wearable sensors for acoustic noise, heat (temp), particle number counts (PNC) and geo-location (GPS) in different environments. We present an extensive device inter-comparison and a ranking of the sensors based on performance measures, Taylor diagrams, Bland-Altman plots, and ease-of-use aspects. The sensors showed moderate to high correlations with precision reference devices (r = 0.4-0.99). Differences between errors outdoors and indoors suggest that environmental conditions have impact upon the accuracy of the sensors. Reaction time, recording interval, and sensor ventilation are features that play a crucial role for both ease-of-use and accuracy. We conclude with a final performance () ranking: (GPS) >  (noise) >  (temp) >  (PNC). The results are relevant for future epidemiological studies of multifactorial exposure of individuals and their health and should guide the selection of wearables when persons are involved that are technically untaught. Inferences from multifactorial data are based on the performance of all sensors and the weakest chain links are PNC and temp sensors for which our article recommends urgent improvements.

Kids in space: Measuring children's residential neighborhoods and other destinations using activity space GPS and wearable camera data

INTRODUCTION

Defining the boundary of children's 'neighborhoods' has important implications for understanding the contextual influences on child health. Additionally, insight into activities that occur outside people's neighborhoods may indicate exposures that place-based studies cannot detect. This study aimed to 1) extend current neighborhood research, using data from wearable cameras and GPS devices that were worn over several days in an urban setting; 2) define the boundary of children's neighborhoods by using leisure time activity space data; and 3) determine the destinations visited by children in their leisure time, outside their neighborhoods.

METHOD

One hundred and fourteen children (mean age 12y) from Wellington, New Zealand wore wearable cameras and GPS recorders. Residential Euclidean buffers at incremental distances were paired with GPS data (thereby identifying time spent in different places) to explore alternative definitions of neighborhood boundaries. Children's neighborhood boundary was at 500 m. A newly developed software application was used to identify 'destinations' visited outside the neighborhood by specifying space-time parameters. Image data from wearable cameras were used to determine the type of destination.

RESULTS

Children spent over half of their leisure time within 500 m of their homes. Children left their neighborhood predominantly to visit school (for leisure purposes), other residential locations (e.g. to visit friends) and food retail outlets (e.g. convenience stores, fast food outlets). Children spent more time at food retail outlets than at structured sport and in outdoor recreation locations combined.

CONCLUSION

Person-centered neighborhood definitions may serve to better represent children's everyday experiences and neighborhood exposures than previous methods based on place-based measures. As schools and other residential locations (friends and family) are important destinations outside the neighborhood, such destinations should be taken into account. The combination of image data and activity space GPS data provides a more robust approach to understanding children's neighborhoods and activity spaces.

Field evaluation of a wearable multimodal soldier navigation system

Challenging environments pose difficulties for terrain navigation, and therefore wearable and multimodal navigation systems have been proposed to overcome these difficulties. Few such navigation systems, however, have been evaluated in field conditions. We evaluated how a multimodal system can aid in navigating in a forest in the context of a military exercise. The system included a head-mounted display, headphones, and a tactile vibrating vest. Visual, auditory, and tactile modalities were tested and evaluated using unimodal, bimodal, and trimodal conditions. Questionnaires, interviews and observations were used to evaluate the advantages and disadvantages of each modality and their multimodal use. The guidance was considered easy to interpret and helpful in navigation. Simplicity of the displayed information was required, which was partially conflicting with the request for having both distance and directional information available.

Monitoring Location-Specific Physical Activity via Integration of Accelerometry and Geotechnology Within Patients With or At Risk of Diabetic Foot Ulcers: A Technological Report

OBJECTIVE

Physical activity variability is a risk factor for diabetic foot ulcers (DFU). Geographic context may influence variability. This study developed initial methods for monitoring location-specific physical activity in this population. Secondarily, preliminary comparisons in location-specific physical activity were made between patients at risk versus patients with active DFU.

METHODS

Five at-risk and 5 actively ulcerated patients were monitored continuously for 72 hours with physical activity and GPS monitors. A custom algorithm time synchronized the 2 devices' data.

RESULTS

On average for all 10 subjects, 1.5 ± 2.1% of activity lacked a corresponding GPS location. 80 ± 11% of self-reported activity events per subject had a GPS identified location. The GPS identified locations were in agreement with the self-reported locations 98 ± 6% of the time. DFU participants' weight-bearing activity was 188% higher at home than away from home. At-risk participants showed similar weight-bearing activity at home as active DFU participants, however, at-risk participants had 132% more weight-bearing activity away-from-home.

CONCLUSIONS

Objectively monitoring location-specific physical activity proved feasible. Future studies using such methodology may enhance understanding of pathomechanics and treatment of DFU.

Wearable Devices for Sports: New Integrated Technologies Allow Coaches, Physicians, and Trainers to Better Understand the Physical Demands of Athletes in Real time

Elite-level athletes and professional sports teams are continually searching for opportunities to improve athletic performance and gain a competitive advantage on the field. Advances in technology have provided new avenues to maximize player health and safety. Over the last decade, time?motion analysis systems, such as video recording and computer digitization, have been used to measure human locomotion and improve sports performance. While these techniques were state of the art at the time, their usefulness is inhibited by the questionable validity of the acquired data, the labor-intensive nature of collecting data with manual hand-notation techniques, and their inability to track athlete position, movement, displacement, and velocity.

Drones may be used to save lives in out of hospital cardiac arrest due to drowning

BACKGROUND

Drowning leading to out-of-hospital cardiac arrest (OHCA) and death is a major public health concern. Submersion with duration of less than 10min is associated with favorable neurological outcome and nearby bystanders play a considerable role in rescue and resuscitation. Drones can provide a visual overview of an accident scene, their potential as lifesaving tools in drowning has not been evaluated.

AIM

The aim of this simulation study was to evaluate the efficiency of a drone for providing earlier location of a submerged possible drowning victim in comparison with standard procedure.

METHOD

This randomized simulation study used a submerged manikin placed in a shallow (<2m) 100×100-m area at Tylösand beach, Sweden. A search party of 14 surf-lifeguards (control) was compared to a drone transmitting video to a tablet (intervention). Time from start to contact with the manikin was the primary endpoint.

RESULTS

Twenty searches were performed in total, 10 for each group. The median time from start to contact with the manikin was 4:34min (IQR 2:56-7:48) for the search party (control) and 0:47min (IQR 0:38-0:58) for the drone-system (intervention) respectively (p<0.001). The median time saved by using the drone was 3:38min (IQR 2:02-6:38).

CONCLUSION

A drone transmitting live video to a tablet is feasible, time saving in comparison to traditional search parties and may be used for providing earlier location of submerged victims at a beach. Drone search can possibly contribute to earlier onset of CPR in drowning victims.

The potential for LiDAR technology to map fire fuel hazard over large areas of Australian forest

Fuel load is a primary determinant of fire spread in Australian forests. In east Australian forests, litter and canopy fuel loads and hence fire hazard are thought to be highest at and beyond steady-state fuel loads 15-20 years post-fire. Current methods used to predict fuel loads often rely on course-scale vegetation maps and simple time-since-fire relationships which mask fine-scale processes influencing fuel loads. Here we use Light Detecting and Remote Sensing technology (LiDAR) and field surveys to quantify post-fire mid-story and crown canopy fuel accumulation and fire hazard in Dry Sclerophyll Forests of the Sydney Basin (Australia) at fine spatial-scales (20 × 20 m cell resolution). Fuel cover was quantified in three strata important for crown fire propagation (0.5-4 m, 4-15 m, >15 m) over a 144 km(2) area subject to varying fire fuel ages. Our results show that 1) LiDAR provided a precise measurement of fuel cover in each strata and a less precise but still useful predictor of surface fuels, 2) cover varied greatly within a mapped vegetation class of the same fuel age, particularly for elevated fuel, 3) time-since-fire was a poor predictor of fuel cover and crown fire hazard because fuel loads important for crown fire propagation were variable over a range of fire fuel ages between 2 and 38 years post-fire, and 4) fuel loads and fire hazard can be high in the years immediately following fire. Our results show the benefits of spatially and temporally specific in situ fuel sampling methods such as LiDAR, and are widely applicable for fire management actions which aim to decrease human and environmental losses due to wildfire.

Field Evaluations of Tracking/Locating Technologies for Prevention of Missing Incidents

BACKGROUND

Persons with dementia are at risk of a missing incident, which is defined as an instance in which a demented person's whereabouts are unknown to the caregiver and the individual is not in an expected location. Since it is critical to determine the missing person's location as quickly as possible, we evaluated whether commercially available tracking technologies can assist in a rapid recovery.

METHODS

This study examined 7 commercially available tracking devices: 3 radio frequency (RF) based and 4 global positioning system (GPS) based, employing realistic tracking scenarios. Outcome measures were time to discovery and degree of deviation from a straight intercept course.

RESULTS/CONCLUSION

Across all scenarios tested, GPS devices were found to be approximately twice as efficient as the RF devices in locating a "missing person." While the RF devices showed reasonable performance at close proximity, the GPS devices were found to be more appropriate overall for tracking/locating missing persons over unknown and larger distances.

What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement

BACKGROUND

With the proliferation of new mobile and in-vehicle technologies, understanding the motivations behind a driver's voluntary engagement with such technologies is crucial from a safety perspective, yet is complex. Previous literature either surveyed a large number of distractions that may be diverse, or too focuses on one particular activity, such as cell phone use. Further, earlier studies about social-psychological factors underlying driver distraction tend to focus on one or two factors in-depth, and those that examine a more comprehensive set of factors are often limited in their analyses methods.

OBJECTIVE

The present work considers a wide array of social-psychological factors within a structural equation model to predict their influence on a focused set of technology-based distractions. A better understanding of these facilitators can enhance the design of distraction mitigation strategies.

METHOD

We analysed survey responses about three technology-based driver distractions: holding phone conversations, manually interacting with cell phones, and adjusting the settings of in-vehicle technology, as well as responses on five social-psychological factors: attitude, descriptive norm, injunctive norm, technology inclination, and a risk/sensation seeking personality. Using data collected from 525 drivers (ages: 18-80), a structural equation model was built to analyse these social-psychological factors as latent variables influencing self-reported engagement in these three technology-based distractions.

RESULTS

Self-reported engagement in technology-based distractions was found to be largely influenced by attitudes about the distractions. Personality and social norms also played a significant role, but technology inclination did not. A closer look at two age groups (18-30 and 30+) showed that the effect of social norms, especially of injunctive norm (i.e., perceived approvals), was less prominent in the 30+ age group, while personality remained a significant predictor for the 30+ age group but marginally significant for the younger group.

CONCLUSION

Findings from this work provide insights into the social-psychological factors behind intentional engagement in technology-based distractions and in particular suggesting that these factors may be sensitive to demographic differences.

Merge Fuzzy Visual Servoing and GPS-Based Planning to Obtain a Proper Navigation Behavior for a Small Crop-Inspection Robot

The concept of precision agriculture, which proposes farming management adapted to crop variability, has emerged in recent years. To effectively implement precision agriculture, data must be gathered from the field in an automated manner at minimal cost. In this study, a small autonomous field inspection vehicle was developed to minimise the impact of the scouting on the crop and soil compaction. The proposed approach integrates a camera with a GPS receiver to obtain a set of basic behaviours required of an autonomous mobile robot to inspect a crop field with full coverage. A path planner considered the field contour and the crop type to determine the best inspection route. An image-processing method capable of extracting the central crop row under uncontrolled lighting conditions in real time from images acquired with a reflex camera positioned on the front of the robot was developed. Two fuzzy controllers were also designed and developed to achieve vision-guided navigation. A method for detecting the end of a crop row using camera-acquired images was developed. In addition, manoeuvres necessary for the robot to change rows were established. These manoeuvres enabled the robot to autonomously cover the entire crop by following a previously established plan and without stepping on the crop row, which is an essential behaviour for covering crops such as maize without damaging them.

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

The « Intelligent Wardrobe »

In an ageing society technical systems that support the residents at home are becoming increasingly important. Many of the technologies available today focus on detecting falls or monitoring the health of residents. There are a few projects that focus the « smart home for the elderly » and offer support for the daily activities. The Institute of Medical Informatics of the Bern University of Applied Sciences has developed a prototype of an intelligent wardrobe. Based on sensor data from the apartment like inside temperature, weather forecast and todays events suggestions for appropriate clothes are generated and shown on a display. To facilitate the search, the garments are marked in the closet with colored LEDs.

Emulating avian orographic soaring with a small autonomous glider

This paper explores a method by which an unpowered, fixed-wing micro air vehicle (MAV) may autonomously gain height by utilising orographic updrafts in urban environments. These updrafts are created when wind impinges on both man-made and natural obstacles, and are often highly turbulent and very localised. Thus in contrast to most previous autonomous soaring research, which have focused on large thermals and ridges, we use a technique inspired by kestrels known as 'wind-hovering', in order to maintain unpowered flight within small updrafts. A six-degree-of-freedom model of a MAV was developed based on wind-tunnel tests and vortex-lattice calculations, and the model was used to develop and test a simple cascaded control system designed to hold the aircraft on a predefined trajectory within an updraft. The wind fields around two typical updraft locations (a building and a hill) were analysed, and a simplified trajectory calculation method was developed by which trajectories for height gain can be calculated on-board the aircraft based on a priori knowledge of the wind field. The results of simulations are presented, demonstrating the behaviour of the system in both smooth and turbulent flows. Finally, the results from a series of flight tests are presented. Flight tests at the hill were consistently successful, while flights around the building could not be sustained for periods of more than approximately 20 s. The difficulty of operating near a building is attributable to significant levels of low-frequency unsteadiness (gustiness) in the oncoming wind during the flight tests, effectively resulting in a loss of updraft for sustained periods.

Quantifying the physical activity energy expenditure of commuters using a combination of global positioning system and combined heart rate and movement sensors

BACKGROUND

Active commuting may help to increase adults' physical activity levels. However, estimates of its energy cost are derived from a small number of studies which are laboratory-based or use self-reported measures.

METHODS

Adults working in Cambridge (UK) recruited through a predominantly workplace-based strategy wore combined heart rate and movement sensors and global positioning system (GPS) devices for one week, and completed synchronous day-by-day travel diaries in 2010 and 2011. Commuting journeys were delineated using GPS data, and metabolic intensity (standard metabolic equivalents; MET) was derived and compared between journey types using mixed-effects linear regression.

RESULTS

182 commuting journeys were included in the analysis. Median intensity was 1.28 MET for car journeys; 1.67 MET for bus journeys; 4.61 MET for walking journeys; 6.44 MET for cycling journeys; 1.78 MET for journeys made by car in combination with walking; and 2.21 MET for journeys made by car in combination with cycling. The value for journeys made solely by car was significantly lower than those for all other journey types (p<0.04). On average, 20% of the duration of journeys incorporating any active travel (equating to 8 min) was spent in moderate-to-vigorous physical activity (MVPA).

CONCLUSIONS

We have demonstrated how GPS and activity data from a free-living sample can be used simultaneously to provide objective estimates of commuting energy expenditure. On average, incorporating walking or cycling into longer journeys provided over half the weekly recommended activity levels from the commute alone. This may be an efficient way of achieving physical activity guidelines and improving population health.

"Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health": Correction to Ben-Zeev et al. (2015)

Reports an error in "Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health" by Dror Ben-Zeev, Emily A. Scherer, Rui Wang, Haiyi Xie and Andrew T. Campbell (Psychiatric Rehabilitation Journal, 2015[Sep], Vol 38[3], 218-226). Model fit statistics in Table 1 are reported as a row for Model 2, but not for Model 1, due to a production error. Model 1 fit statistics should appear as a row with the following information: 2LL 1490.0, AIC 1498.0 & BIC 1505.3. (The following abstract of the original article appeared in record 2015-14736-001.)

OBJECTIVE

Optimal mental health care is dependent upon sensitive and early detection of mental health problems. We have introduced a state-of-the-art method for the current study for remote behavioral monitoring that transports assessment out of the clinic and into the environments in which individuals negotiate their daily lives. The objective of this study was to examine whether the information captured with multimodal smartphone sensors can serve as behavioral markers for one's mental health. We hypothesized that (a) unobtrusively collected smartphone sensor data would be associated with individuals' daily levels of stress, and (b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time.

METHOD

A total of 47 young adults (age range: 19-30 years) were recruited for the study. Individuals were enrolled as a single cohort and participated in the study over a 10-week period. Participants were provided with smartphones embedded with a range of sensors and software that enabled continuous tracking of their geospatial activity (using the Global Positioning System and wireless fidelity), kinesthetic activity (using multiaxial accelerometers), sleep duration (modeled using device-usage data, accelerometer inferences, ambient sound features, and ambient light levels), and time spent proximal to human speech (i.e., speech duration using microphone and speech detection algorithms). Participants completed daily ratings of stress, as well as pre- and postmeasures of depression (Patient Health Questionnaire-9; Spitzer, Kroenke, & Williams, 1999), stress (Perceived Stress Scale; Cohen et al., 1983), and loneliness (Revised UCLA Loneliness Scale; Russell, Peplau, & Cutrona, 1980).

RESULTS

Mixed-effects linear modeling showed that sensor-derived geospatial activity (p < .05), sleep duration (p < .05), and variability in geospatial activity (p < .05), were associated with daily stress levels. Penalized functional regression showed associations between changes in depression and sensor-derived speech duration (p < .05), geospatial activity (p < .05), and sleep duration (p < .05). Changes in loneliness were associated with sensor-derived kinesthetic activity (p < .01).

CONCLUSIONS AND IMPLICATIONS FOR PRACTICE

Smartphones can be harnessed as instruments for unobtrusive monitoring of several behavioral indicators of mental health. Creative leveraging of smartphone sensing could provide novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceeds what is currently feasible with existing assessment technologies. (PsycINFO Database Record

Distributed Storage Algorithm for Geospatial Image Data Based on Data Access Patterns

Declustering techniques are widely used in distributed environments to reduce query response time through parallel I/O by splitting large files into several small blocks and then distributing those blocks among multiple storage nodes. Unfortunately, however, many small geospatial image data files cannot be further split for distributed storage. In this paper, we propose a complete theoretical system for the distributed storage of small geospatial image data files based on mining the access patterns of geospatial image data using their historical access log information. First, an algorithm is developed to construct an access correlation matrix based on the analysis of the log information, which reveals the patterns of access to the geospatial image data. Then, a practical heuristic algorithm is developed to determine a reasonable solution based on the access correlation matrix. Finally, a number of comparative experiments are presented, demonstrating that our algorithm displays a higher total parallel access probability than those of other algorithms by approximately 10–15% and that the performance can be further improved by more than 20% by simultaneously applying a copy storage strategy. These experiments show that the algorithm can be applied in distributed environments to help realize parallel I/O and thereby improve system performance.

Quantifying the spatial and temporal variation in dose from external exposure to radiation: a new tool for use on free-ranging wildlife

Inadequate dosimetry is often the fundamental problem in much of the controversial research dealing with radiation effects on free-ranging wildlife. Such research is difficult because of the need to measure dose from several potential pathways of exposure (i.e., internal contamination, external irradiation, and inhalation). Difficulties in quantifying external exposures can contribute significantly to the uncertainties of dose-effect relationships. Quantifying an animal's external exposure due to spatial-temporal use of habitats that can vary by orders of magnitude in radiation levels is particularly challenging. Historically, wildlife dosimetry studies have largely ignored or been unable to accurately quantify variability in external dose because of technological limitations. The difficulties of quantifying the temporal-spatial aspects of external irradiation prompted us to develop a new dosimetry instrument for field research. We merged two existing technologies [Global Positioning Systems (GPS) and electronic dosimeters] to accommodate the restrictive conditions of having a combined unit small enough to be unobtrusively worn on the neck of a free-ranging animal, and sufficiently robust to withstand harsh environmental conditions. The GPS-dosimeter quantifies the spatial and temporal variation in external dose as wildlife traverse radioactively contaminated habitats and sends, via satellites, an animal's location and short term integrated dose to the researcher at a user-defined interval. Herein we describe: (1) the GPS-dosimeters; (2) tests to compare their uniformity of response to external irradiation under laboratory conditions; (3) field tests of their durability when worn on wildlife under natural conditions; and (4) a field application of the new technology at a radioactively contaminated site. Use of coupled GPS-dosimetry will allow, for the first time, researchers to better understand the relationship of animals to their contaminated habitats and better assess animal responses to the stress of radiological exposures.

The Outdoor MEDIA DOT: The development and inter-rater reliability of a tool designed to measure food and beverage outlets and outdoor advertising

Few studies of the food environment have collected primary data, and even fewer have reported reliability of the tool used. This study focused on the development of an innovative electronic data collection tool used to document outdoor food and beverage (FB) advertising and establishments near 43 middle and high schools in the Outdoor MEDIA Study. Tool development used GIS based mapping, an electronic data collection form on handheld devices, and an easily adaptable interface to efficiently collect primary data within the food environment. For the reliability study, two teams of data collectors documented all FB advertising and establishments within one half-mile of six middle schools. Inter-rater reliability was calculated overall and by advertisement or establishment category using percent agreement. A total of 824 advertisements (n=233), establishment advertisements (n=499), and establishments (n=92) were documented (range=8-229 per school). Overall inter-rater reliability of the developed tool ranged from 69-89% for advertisements and establishments. Results suggest that the developed tool is highly reliable and effective for documenting the outdoor FB environment.

Bosnian and American students' attitudes toward electronic monitoring: is it about what we know or where we come from?

The use of community corrections continues to grow across the globe as alternatives to incarceration are sought. Little research attention, however, has been directed at correctional alternatives from a global orientation. The purpose of this research study is to compare the way that a sample of criminal justice students from the United States (n = 118) and Bosnia and Herzegovina (n = 133) perceive electronic monitoring. Because electronic monitoring is a newer sentencing alternative and it is used differently in Bosnia and Herzegovina than it is in the United States, it is predicted that Bosnian students will view electronic monitoring differently than will students from the United States. This study finds that while students are largely supportive of electronic monitoring sentences, support is affected by offender type and student nationality. For example, Bosnian students are more supportive of electronic monitoring sentences for drug offenders while American students are more supportive of electronic monitoring sentences for juvenile offenders. Differences were also found across student groups when attitudes toward electronic monitoring and the costs and pains associated with electronic monitoring were assessed. Specifically, American students were less likely to view electronic monitoring as meeting the goals of rehabilitation and more likely to view the conditions and restrictions associated with electronic monitoring as being punitive than Bosnian students were. Implications from these findings, as well as limitations and suggestions for further research are discussed.

[Establishment of Oncomelania hupensis snail database based on smartphone and Google Earth]

OBJECTIVE

To establish an Oncomelania hupensis snail database based on smartphone and Google Earth.

METHODS

The HEAD GPS software was loaded in the smartphone first. The GPS data of the snails were collected by the smartphone. The original data were exported to the computer with the format of KMIUKMZ. Then the data were converted into Excel file format by using some software. Finally, the results based on laboratory were filled, and the digital snail data were established. The data were converted into KML, and then were showed by Google Earth visually.

RESULTS

The snail data of a 5 hm2-beach along the Yangtze River were collected and the distribution of the snails based on Google Earth was obtained. The database of the snails was built. The query function was implemented about the number of the total snails, the living snails and the schistosome infected snails of each survey frame.

CONCLUSION

The digital management of the snail data is realized by using the smartphone and Google Earth.

A WSN healthcare monitoring system for elderly people in geriatric facilities

This paper investigates an enhanced WSN based monitoring system for elderly people in geriatric facilities. Apart from general physical monitoring functionalities, we explore the potential of using a wireless audio module to perform mental health monitoring. The telephone version of the mini mental status examination (T-MMSE) was adopted in the mental health monitoring and the RSSI (Received Signal Strength Indicator) localization algorithm was implemented in this system to track elderly people's the real-time location and send alerts based on their inactivity/activity levels, movement history and entry into restricted zones. Experimental test was performed to evaluate the performance of the proposed system. With results obtained and studied, this work can be extended to perform detection and rehabilitation function for elderly people with mental illness. This kind of monitoring system would be expected to make an important impact on many application scenarios for geriatric facilities.

Lightweight aerial vehicles for monitoring, assessment and mapping of radiation anomalies

The Fukushima Daiichi nuclear power plant (FDNPP) incident released a significant mass of radioactive material into the atmosphere. An estimated 22% of this material fell out over land following the incident. Immediately following the disaster, there was a severe lack of information not only pertaining to the identity of the radioactive material released, but also its distribution as fallout in the surrounding regions. Indeed, emergency aid groups including the UN did not have sufficient location specific radiation data to accurately assign exclusion and evacuation zones surrounding the plant in the days and weeks following the incident. A newly developed instrument to provide rapid and high spatial resolution assessment of radionuclide contamination in the environment is presented. The device consists of a low cost, lightweight, unmanned aerial platform with a microcontroller and integrated gamma spectrometer, GPS and LIDAR. We demonstrate that with this instrument it is possible to rapidly and remotely detect ground-based radiation anomalies with a high spatial resolution (<1 m). Critically, as the device is remotely operated, the user is removed from any unnecessary or unforeseen exposure to elevated levels of radiation.

A novel angle computation and calibration algorithm of bio-inspired sky-light polarization navigation sensor

Navigation plays a vital role in our daily life. As traditional and commonly used navigation technologies, Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) can provide accurate location information, but suffer from the accumulative error of inertial sensors and cannot be used in a satellite denied environment. The remarkable navigation ability of animals shows that the pattern of the polarization sky can be used for navigation. A bio-inspired POLarization Navigation Sensor (POLNS) is constructed to detect the polarization of skylight. Contrary to the previous approach, we utilize all the outputs of POLNS to compute input polarization angle, based on Least Squares, which provides optimal angle estimation. In addition, a new sensor calibration algorithm is presented, in which the installation angle errors and sensor biases are taken into consideration. Derivation and implementation of our calibration algorithm are discussed in detail. To evaluate the performance of our algorithms, simulation and real data test are done to compare our algorithms with several exiting algorithms. Comparison results indicate that our algorithms are superior to the others and are more feasible and effective in practice.

Testing VHF/GPS collar design and safety in the study of free-roaming horses

Effective and safe monitoring techniques are needed by U.S. land managers to understand free-roaming horse behavior and habitat use and to aid in making informed management decisions. Global positioning system (GPS) and very high frequency (VHF) radio collars can be used to provide high spatial and temporal resolution information for detecting free-roaming horse movement. GPS and VHF collars are a common tool used in wildlife management, but have rarely been used for free-roaming horse research and monitoring in the United States. The purpose of this study was to evaluate the design, safety, and detachment device on GPS/VHF collars used to collect free-roaming horse location and movement data. Between 2009 and 2010, 28 domestic and feral horses were marked with commercial and custom designed VHF/GPS collars. Individual horses were evaluated for damage caused by the collar placement, and following initial observations, collar design was modified to reduce the potential for injury. After collar modifications, which included the addition of collar length adjustments to both sides of the collar allowing for better alignment of collar and neck shapes, adding foam padding to the custom collars to replicate the commercial collar foam padding, and repositioning the detachment device to reduce wear along the jowl, we observed little to no evidence of collar wear on horses. Neither custom-built nor commercial collars caused injury to study horses, however, most of the custom-built collars failed to collect data. During the evaluation of collar detachment devices, we had an 89% success rate of collar devices detaching correctly. This study showed that free-roaming horses can be safely marked with GPS and/or VHF collars with minimal risk of injury, and that these collars can be a useful tool for monitoring horses without creating a risk to horse health and wellness.

Is change in environmental supportiveness between primary and secondary school associated with a decline in children׳s physical activity levels?

Using a sample of English schoolchildren, we evaluate whether a change in school local area environmental supportiveness between primary and secondary school is associated with changes in active travel behaviours and physical activity levels. Participant׳s activity levels and travel behaviours were recorded for a week during their primary school final year and secondary school first year. Environmental supportiveness was evaluated using a Geographical Information System. Children attending both a primary and secondary school with a more supportive local environment were more likely to maintain active travel behaviours than those with less supportive environments. However, no trends were apparent with change in school supportiveness and change in physical activity. Policies that focus on the maintenance and uptake of active travel behaviours may help maintain children׳s physical activity levels into adolescence.

A comprehensive method for GNSS data quality determination to improve ionospheric data analysis

Global Navigation Satellite Systems (GNSS) are now recognized as cost-effective tools for ionospheric studies by providing the global coverage through worldwide networks of GNSS stations. While GNSS networks continue to expand to improve the observability of the ionosphere, the amount of poor quality GNSS observation data is also increasing and the use of poor-quality GNSS data degrades the accuracy of ionospheric measurements. This paper develops a comprehensive method to determine the quality of GNSS observations for the purpose of ionospheric studies. The algorithms are designed especially to compute key GNSS data quality parameters which affect the quality of ionospheric product. The quality of data collected from the Continuously Operating Reference Stations (CORS) network in the conterminous United States (CONUS) is analyzed. The resulting quality varies widely, depending on each station and the data quality of individual stations persists for an extended time period. When compared to conventional methods, the quality parameters obtained from the proposed method have a stronger correlation with the quality of ionospheric data. The results suggest that a set of data quality parameters when used in combination can effectively select stations with high-quality GNSS data and improve the performance of ionospheric data analysis.

Indoor magnetic signature based localization algorithm without person-dependent parameter calibration

Location-dependent differences of ambient magnetic fields inside a building can be used to estimate location. In this paper, an inertial/magnetic sensor is attached to a belt position and its location is estimated using the ambient magnetic field. The walking distance is estimated using the linear relationship between the walking step length and the maximum acceleration during the step. The magnetic field data during walking is compared with a pre-collected magnetic signature. In this process, calibration steps are required for two person-dependent parameters: the walking step length estimation parameter and the hard iron parameter. An adaptive algorithm is proposed, in which these person-dependent parameters are estimated in addition to the location. Thus no person-dependent parameter calibration process is required. Through experiments, it is shown that the location and parameters are estimated accurately.