Software Defined Radio for GNSS Radio Frequency Interference Localization.

The use of radio direction finding techniques in order to identify and reject harmful interference has been a topic of discussion both past and present for signals in the GNSS bands. Advances in commercial off-the-shelf radio hardware have led to the development of new low-cost, compact, phase coherent receiver platforms such as the KrakenSDR from KrakenRF whose testing and characterization will be the primary focus of this paper. Although not specifically designed for GNSSs, the capabilities of this platform are well aligned with the needs of GNSSs. Testing results from both benchtop and in the field will be displayed which verify the KrakenSDR's phase coherence and angle of arrival estimates to array dependent resolution bounds. Additionally, other outputs from the KrakenSDR such as received signal strength indicators and the angle of arrival confidence values show strong connections to angle of arrival estimate quality. Within this work the testing that will be primarily presented is at 900 MHz, with results presented from a government-sponsored event where the Kraken was tested at 1575.42 MHz. Finally, a discussion of calibration of active antenna arrays for angle of arrival is included as the introduction of active antenna elements used in GNSS signal collection can influence angle of arrival estimation.

A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS.

A fully integrated low-power area-efficient receiver using a low-intermediate frequency topology for BDS-3 and GPS L1 bands is presented in this paper. Accurate localization can be achieved without requiring off-chip low-noise amplifiers. The receiver bandwidths for GPS and BDS-3 are 2 MHz and 4 MHz, respectively. Digitally assisted calibration schemes, such as RC calibration, automatic gain control, and DC offset correction are integrated to resist the effects of the process, voltage, and temperature (PVT) variations. The receiver-fabricated in a standard 55 nm CMOS technology-provides a maximum gain of 113.2 dB, a gain control range of 61 dB, and a minimum noise figure of 1.74 dB under a 1.2 V supply. The receiver, with and without the frequency synthesizer that provides the local oscillator frequency, consumes 8.7 mA and 4.8 mA, with areas of 0.73 mm2 and 0.345 mm2, respectively.

A Simultaneous Localization and Mapping System Using the Iterative Error State Kalman Filter Judgment Algorithm for Global Navigation Satellite System.

Outdoor autonomous mobile robots heavily rely on GPS data for localization. However, GPS data can be erroneous and signals can be interrupted in highly urbanized areas or areas with incomplete satellite coverage, leading to localization deviations. In this paper, we propose a SLAM (Simultaneous Localization and Mapping) system that combines the IESKF (Iterated Extended Kalman Filter) and a factor graph to address these issues. We perform IESKF filtering on LiDAR and inertial measurement unit (IMU) data at the front-end to achieve a more accurate estimation of local pose and incorporate the resulting laser inertial odometry into the back-end factor graph. Furthermore, we introduce a GPS signal filtering method based on GPS state and confidence to ensure that abnormal GPS data is not used in the back-end processing. In the back-end factor graph, we incorporate loop closure factors, IMU preintegration factors, and processed GPS factors. We conducted comparative experiments using the publicly available KITTI dataset and our own experimental platform to compare the proposed SLAM system with two commonly used SLAM systems: the filter-based SLAM system (FAST-LIO) and the graph optimization-based SLAM system (LIO-SAM). The experimental results demonstrate that the proposed SLAM system outperforms the other systems in terms of localization accuracy, especially in cases of GPS signal interruption.

Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation.

In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved.

Mobility enhancement among older adults 75 + in rural areas: Study protocol of the MOBILE randomized controlled trial.

BACKGROUND: Maintaining mobility in old age is crucial for healthy ageing including delaying the onset and progress of frailty. However, the extent of an individuals´ mobility relies largely on their personal, social, and environmental resources as outlined in the Life-Space Constriction Model. Recent studies mainly focus on facilitating habitual out-of-home mobility by fostering one type of resources only. The MOBILE trial aims at testing whether tablet-assisted motivational counselling enhances the mobility of community-dwelling older adults by addressing personal, social, and environmental resources. METHODS: In the MOBILE randomized controlled trial, we plan to enrol 254 community-dwelling older adults aged 75 and older from Havelland, a rural area in Germany. The intervention group will receive a tablet-assisted motivational counselling at the participant´s home and two follow-up telephone sessions. Main focus of the counselling sessions lays on setting and adapting individual mobility goals and applying action planning and habit formation strategies by incorporating the personal social network and regional opportunities for engaging in mobility related activities. The control group will receive postal general health information. The primary mobility outcome is time out-of-home assessed by GPS (GPS.Rec2.0-App) at three points in time (baseline, after one month, and after three months for seven consecutive days each). Secondary outcomes are the size of the GPS-derived life-space convex hull, self-reported life-space mobility (LSA-D), physical activity (IPAQ), depressive symptoms (GDS), frailty phenotype, and health status (SF-12). DISCUSSION: The MOBILE trial will test the effect of a motivational counselling intervention on out-of-home mobility in community-dwelling older adults. Novel aspects of the MOBILE trial include the preventive multi-level intervention approach in combination with easy-to-use technology. The ecological approach ensures low-threshold implementation, which increases the benefit for the people in the region. TRIAL REGISTRATION: The MOBILE trial is prospectively registered at DRKS (Deutsches Register Klinischer Studien, German Registry of Clinical Trials) DRKS00025230 . Registered 5 May 2021.

Research on the Error of Global Positioning System Based on Time Series Analysis.

Due to the poor dynamic positioning precision of the Global Positioning System (GPS), Time Series Analysis (TSA) and Kalman filter technology are used to construct the positioning error of GPS. According to the statistical characteristics of the autocorrelation function and partial autocorrelation function of sample data, the Autoregressive (AR) model which is based on a Kalman filter is determined, and the error model of GPS is combined with a Kalman filter to eliminate the random error in GPS dynamic positioning data. The least square method is used for model parameter estimation and adaptability tests, and the experimental results show that the absolute value of the maximum error of longitude and latitude, the mean square error of longitude and latitude and average absolute error of longitude and latitude are all reduced, and the dynamic positioning precision after correction has been significantly improved.

The spatiotemporal movement of patients in and out of a psychiatric hospital: an observational GPS study.

BACKGROUND: Movement is a basic component of health. Little is known about the spatiotemporal movement of patients with mental disorders. The aim of this study was to determine how spatiotemporal movement of patients related to their symptoms and wellbeing. METHOD: A total of 106 patients (inpatients (n = 69) and outpatients (n = 37)) treated for a wide range of mental disorders (transdiagnostic sample) carried a GPS-enabled smartphone for one week at the beginning of treatment. Algorithms were applied to establish spatiotemporal clusters and subsequently related to known characteristics of these groups (i.e., at the hospital, at home). Symptomatology, Wellbeing, and Psychological flexibility were also assessed. RESULTS: Spatiotemporal patterns of inpatients and outpatients showed differences consistent with predictions (e.g., outpatients showed higher active areas). These patterns were largely unassociated with symptoms (except for agoraphobic symptoms). Greater movement and variety of movement were more predictive of wellbeing, however, in both inpatients and outpatients. CONCLUSION: Measuring spatiotemporal patterns is feasible, predictive of wellbeing, and may be a marker of patient functioning. Ethical issues of collecting GPS data are discussed.

Children's outdoor active mobility behaviour and neighbourhood safety: a systematic review in measurement methods and future research directions.

BACKGROUND: Numerous studies have examined the association between safety and primary school-aged children's forms of active mobility. However, variations in studies' measurement methods and the elements addressed have contributed to inconsistencies in research outcomes, which may be forming a barrier to advancing researchers' knowledge about this field. To assess where current research stands, we have synthesised the methodological measures in studies that examined the effects of neighbourhood safety exposure (perceived and measured) on children's outdoor active mobility behaviour and used this analysis to propose future research directions. METHOD: A systematic search of the literature in six electronic databases was conducted using pre-defined eligibility criteria and was concluded in July 2020. Two reviewers screened the literature abstracts to determine the studies' inclusion, and two reviewers independently conducted a methodological quality assessment to rate the included studies. RESULTS: Twenty-five peer-reviewed studies met the inclusion criteria. Active mobility behaviour and health characteristics were measured objectively in 12 out of the 25 studies and were reported in another 13 studies. Twenty-one studies overlooked spatiotemporal dimensions in their analyses and outputs. Delineations of children's neighbourhoods varied within 10 studies' objective measures, and the 15 studies that opted for subjective measures. Safety perceptions obtained in 22 studies were mostly static and primarily collected via parents, and dissimilarities in actual safety measurement methods were present in 6 studies. The identified schematic constraints in studies' measurement methods assisted in outlining a three-dimensional relationship between 'what' (determinants), 'where' (spatial) and 'when' (time) within a methodological conceptual framework. CONCLUSIONS: The absence of standardised measurement methods among relevant studies may have led to the current diversity in findings regarding active mobility, spatial (locality) and temporal (time) characteristics, the neighbourhood, and the representation of safety. Ignorance of the existing gaps and heterogeneity in measures may impact the reliability of evidence and poses a limitation when synthesising findings, which could result in serious biases for policymakers. Given the increasing interest in children's health studies, we suggested alternatives in the design and method of measures that may guide future evidence-based research for policymakers who aim to improve children's active mobility and safety.

Household sampling through geocoded points and satellite view: A step-by-step approach to implement a spatial sampling method for demographic and health surveys in areas without population sampling frame and with limited resource settings.

INTRODUCTION: Spatial sampling is increasingly used in health surveys as it provides a simple way to randomly select target populations on sites where reliable and complete data on the general population are not available. However, the previously implemented protocols have been poorly detailed, making replication difficult or even impossible. To our knowledge, ours is the first document describing step-by-step an efficient spatial sampling method for health surveys. Our objective is to facilitate the rapid acquisition of the technical skills and know-how necessary for its deployment. METHODS: The spatial sampling design is based on the random generation of geocoded points in the study area. Afterwards, these points were projected on the satellite view of Google Earth Pro™ software and the identified buildings were selected for field visits. A detailed formula of the number of points required, considering non-responses, is proposed. Density of buildings was determined by drawing circles around points and by using a replacement strategy when interviewing was unachievable. The method was implemented for a cross-sectional study during the April-May 2016 period in Cotonou (Bénin). The accuracy of the collected data was assessed by comparing them to those of the Cotonou national census. RESULT: This approach does not require prior displacement in the study area and only 1% of identified buildings with Google Earth Pro™ were no longer extant. Most of the measurements resulting from the general census were within the confidence intervals of those calculated with the sample data. Furthermore, the range of measurements resulting from the general census was similar to those calculated with the sample data. These include, for example, the proportion of the foreign population (unweighted 8.9%/weighted 9% versus 8.5% in census data), the proportion of adults over 17 years of age (56.7% versus 57% in census data), the proportion of households whose head is not educated (unweighted 21.9%/weighted 22.8% versus 21.1% in census data). CONCLUSION: This article illustrates how an epidemiological field survey based on spatial sampling can be successfully implemented at low cost, quickly and with little technical and theoretical knowledge. While statistically similar to simple random sampling, this survey method greatly simplifies its implementation.

Clustering of asymptomatic Plasmodium falciparum infection and the effectiveness of targeted malaria control measures.

BACKGROUND: Because clustering of Plasmodium falciparum infection had been noted previously, the clustering of infection was examined at four field sites in West Africa: Dangassa and Dioro in Mali, Gambissara in The Gambia and Madina Fall in Senegal. METHODS: Clustering of infection was defined by the percent of persons with positive slides for asexual P. falciparum sleeping in a house which had been geopositioned. Data from each site were then tested for spatial, temporal and spatio-temporal clustering in relation to the prevalence of infection from smear surveys. RESULTS: These studies suggest that clustering of P. falciparum infection also affects the effectiveness of control interventions. For example, the clustering of infection in Madina Fall disappeared in 2014-2016 after vector control eliminated the only breeding site in 2013. In contrast, the temporal clustering of infection in Dioro (rainy season of 2014, dry season of 2015) was consistent with the loss of funding for Dioro in the second quarter of 2014 and disappeared when funds again became available in late 2015. The clustering of infection in rural (western) areas of Gambissara was consistent with known rural-urban differences in the prevalence of infection and with the thatched roofs, open eaves and mud walls of houses in rural Gambissara. In contrast, the most intense transmission was in Dangassa, where the only encouraging observation was a lower prevalence of infection in the dry season. Taken together, these results suggest: (a) the transmission of infection was stopped in Madina Fall by eliminating the only known breeding site, (b) the prevalence of infection was reduced in Dioro after financial support became available again for malaria control in the second half of 2015, (c) improvements in housing should improve malaria control by reducing the number of vectors in rural communities such as western Gambissara, and (d) beginning malaria control during the dry season may reduce transmission in hyperendemic areas such as Dangassa. CONCLUSIONS: From a conceptual perspective, testing for spatial, temporal and spatio-temporal clustering based on epidemiologic data permits the generation of hypotheses for the clustering observed and the testing of candidate interventions to confirm or refute those hypotheses.

Transport-related walking among young adults: when and why?

BACKGROUND: The existing smartphones' technology allows for the objective measurement of a person's movements at a fine-grained level of geographic and temporal detail, and in doing so, it mitigates the issues associated with self-report biases and lack of spatial details. This study proposes and evaluates the advantages of using a smartphone app for collecting accurate, fine-grained, and objective data on people's transport-related walking. METHODS: A sample of 142 participants (mostly young adults) was recruited in a large Australian university, for whom the app recorded all their travel activities over two weekdays during August-September 2014. We identified eight main activity nodes which operate as transport-related walking generators. We explored the participants' transport-related walking patterns around and between these activity nodes through the use of di-graphs to better understand patterns of incidental physical activity and opportunities for intervention to increase incidental walking. RESULTS: We found that the educational node - in other samples may be represented by the workplace - is as important as the residential node for generating walking trips. We also found that the likelihood of transport-related walking trips is larger during the daytime, whereas at night time walking trips tend to be longer. We also showed that patterns of transport-related walking relate to the presence of 'chaining' trips in the afternoon period. CONCLUSIONS: The findings of this study show how the proposed data collection and analytic approach can inform urban design to enhance walkability at locations that are likely to generate walking trips. This study's insights can help to shape public education and awareness campaigns that aim to encourage walking trips throughout the day by suggesting locations and times of the day when engaging in these forms of exercise is easiest and least intrusive.

Smartphone-Based Platform for Secure Multi-Hop Message Dissemination in VANETs.

Vehicular ad-hoc Networks (VANETs) are recognized as a cornerstone of Intelligent Transportation Systems (ITS) to enable the exchange of information among vehicles, which is crucial for the provision of safety-related and entertainment applications. However, practical useful realizations of VANETs are still missing, mainly because of the elevated costs and the lack of a final standardization. In this regard, the feasibility of using smartphones as nodes in VANETs has been explored focusing on small-scale deployments to mainly validate single-hop communication capabilities. Moreover, existing smartphone-based platforms do not consider two crucial requirements in VANETs, namely, multi-hop communication and the provision of security services in the message dissemination process. Furthermore, the problem of securing message dissemination in VANETs is generally analyzed through simulation tools, while performance evaluations on smart devices have not been reported so far. In this paper, we aim to fill this void by designing a fully on-device platform for secure multi-hop message dissemination. We address the multi-hop nature of message dissemination in VANETs by integrating a location-based protocol that enables the selection of relay nodes and retransmissions criteria. As a main distinction, the platform incorporates a novel certificateless cryptographic scheme for ensuring data integrity and nodes' authentication, suitable for VANETs lacking of infrastructure.

Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS.

This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results.

A Sensor Fusion Based Nonholonomic Wheeled Mobile Robot for Tracking Control.

In this paper, a detail design procedure of the real-time trajectory tracking for the nonholonomic wheeled mobile robot (NWMR) is proposed. A 9-axis micro electro-mechanical systems (MEMS) inertial measurement unit (IMU) sensor is used to measure the posture of the NWMR, the position information of NWMR and the hand-held device are acquired by global positioning system (GPS) and then transmit via radio frequency (RF) module. In addition, in order to avoid the gimbal lock produced by the posture computation from Euler angles, the quaternion is utilized to compute the posture of the NWMR. Furthermore, the Kalman filter is used to filter out the readout noise of the GPS and calculate the position of NWMR and then track the object. The simulation results show the posture error between the NWMR and the hand-held device can converge to zero after 3.928 seconds for the dynamic tracking. Lastly, the experimental results show the validation and feasibility of the proposed results.

A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors.

The number of wireless sensors in use-for example, the global positioning system (GPS) intelligent sensor-has increased in recent years. These intelligent sensors generate a vast amount of spatiotemporal data, which can assist in finding patterns of movements. These movement patterns can be used to predict the future location of moving objects; for example, the movement of an emergency vehicle can be predicted for health care decision-making. Although there is a body of research work regarding motion trajectory prediction, there are no guidelines for choosing algorithms best suited for individual needs in uncertain and complex situations and as per the application domains. In this paper, we surveyed existing trajectory prediction algorithms. These algorithms are further ranked scientifically in terms of accuracy (performance), ease of use, and best fit as per the available datasets. Our results show three top algorithms, namely NextPlace, the Markov model, and the hidden Markov model. This study can be beneficial for multicriteria decision-making for various disciplines, including health care.

Method for Remote Determination of Object Coordinates in Space Based on Exact Analytical Solution of Hyperbolic Equations.

Accurate remote determination of the object coordinates in 3D space is one of the main questions in many applications. In one of the most popular methods, such determination of the location of an object uses the measurement by receiving an electromagnetic signal transmitted by several spatially distributed base stations (BS). The main problem is that it is necessary to reduce errors and computation time. To overcome these difficulties, an analytical method for determining the position of an object based on the analysis of time difference of arrival (TDoA) of signals from the transmitter of the object to the receivers of the BS is proposed. One of the main advantages of this method is that it is possible to eliminate the ambiguity in determining the coordinates of the object in space and to increase the accuracy of determining the coordinates when the TDoA measurement between base stations fluctuates. Applications for autonomous automotive vehicles and spacebased positioning systems are analyzed. The results obtained show that the proposed algorithm has an accuracy of determining coordinates several times higher than the method of linearization of hyperbolic equations and is less sensitive to TDoA fluctuations at base stations.

Statistical Distribution Analysis of Navigation Positioning System Errors-Issue of the Empirical Sample Size.

Positioning systems are used to determine position coordinates in navigation (air, land, and marine). Statistical analysis of their accuracy assumes that the position errors (latitude-δφ and longitude-δλ) are random and that their distributions are consistent with the normal distribution. However, in practice, these errors do not appear in a random way, since the position determination in navigation systems is done with an iterative method. It causes so-called "Position Random Walk", similar to the term "Random Walk" known from statistics. It results in the empirical distribution of δφ and δλ being inconsistent with the normal distribution, even for samples of up to several thousand measurements. This phenomenon results in a significant overestimation of the accuracy of position determination calculated from such a short series of measurements, causing these tests to lose their representativeness. This paper attempts to determine the length of a measurement session (number of measurements) that is representative of the positioning system. This will be a measurement session of such a length that the position error statistics (δφ and δλ) represented by the standard deviation values are close to the real values and the calculated mean values (φ¯ and λ¯) are also close to the real values. Special attention will also be paid to the selection of an appropriate (statistically reliable) number of measurements to be tested statistically to verify the hypothesis that the δφ and δλ distributions are consistent with the normal distribution. Empirical measurement data are taken from different positioning systems: Global Positioning System (GPS) (168'286 fixes), Differential Global Positioning System (DGPS) (864'000 fixes), European Geostationary Navigation Overlay Service (EGNOS) (928'492 fixes), and Decca Navigator system (4052 fixes). The analyses showed that all researched positioning systems (GPS, DGPS, EGNOS and Decca Navigator) are characterized by the Position Random Walk (PRW), which resulted in that the empirical distribution of δφ and δλ being inconsistent with the normal distribution. The size of the PRW depends on the nominal accuracy of position determination by the system. It was found that measurement sessions consisting of 1000 fixes (for the GPS system) overestimate the accuracy analysis results by 109.1% and cannot be considered representative. Furthermore, when analyzing the results of long measurement campaigns (GPS and DGPS), it was found that the representative length of the measurement session differs for each positioning system and should be determined for each of them individually.

Embodied Emotion Recognition Based on Life-Logging.

Embodied emotion is associated with interaction among a person's physiological responses, behavioral patterns, and environmental factors. However, most methods for determining embodied emotion has been considered on only fragmentary independent variables and not their inter-connectivity. This study suggests a method for determining the embodied emotion considering interactions among three factors: the physiological response, behavioral patterns, and an environmental factor based on life-logging. The physiological response was analyzed as heart rate variability (HRV) variables. The behavioral pattern was calculated from features of Global Positioning System (GPS) locations that indicate spatiotemporal property. The environmental factor was analyzed as the ambient noise, which is an external stimulus. These data were mapped with the emotion of that time. The emotion was evaluated on a seven-point scale for arousal level and valence level according to Russell's model of emotion. These data were collected from 79 participants in daily life for two weeks. Their relationships among data were analyzed by the multiple regression analysis, after pre-processing the respective data. As a result, significant differences between the arousal level and valence level of emotion were observed based on their relations. The contributions of this study can be summarized as follows: (1) The emotion was recognized in real-life for a more practical application; (2) distinguishing the interactions that determine the levels of arousal and positive emotion by analyzing relationships of individuals' life-log data. Through this, it was verified that emotion can be changed according to the interaction among the three factors, which was overlooked in previous emotion recognition.

AI-Based Sensor Information Fusion for Supporting Deep Supervised Learning.

In recent years, artificial intelligence (AI) and its subarea of deep learning have drawn the attention of many researchers. At the same time, advances in technologies enable the generation or collection of large amounts of valuable data (e.g., sensor data) from various sources in different applications, such as those for the Internet of Things (IoT), which in turn aims towards the development of smart cities. With the availability of sensor data from various sources, sensor information fusion is in demand for effective integration of big data. In this article, we present an AI-based sensor-information fusion system for supporting deep supervised learning of transportation data generated and collected from various types of sensors, including remote sensed imagery for the geographic information system (GIS), accelerometers, as well as sensors for the global navigation satellite system (GNSS) and global positioning system (GPS). The discovered knowledge and information returned from our system provides analysts with a clearer understanding of trajectories or mobility of citizens, which in turn helps to develop better transportation models to achieve the ultimate goal of smarter cities. Evaluation results show the effectiveness and practicality of our AI-based sensor information fusion system for supporting deep supervised learning of big transportation data.

Variations of external load variables between medium- and large-sided soccer games in professional players.

This study compared the relative physical demands of official matches and sided games (medium and large) in professional soccer players by means of a global positioning system. Twenty-three professional male soccer players (24.63[Formula: see text]2.84 years old; 180.94[Formula: see text]6.49 cm; 77.19[Formula: see text]6.46 kg; 52.99[Formula: see text]5.01 VO2max) participated in the study. Total distance, running distance, sprinting distance, number of sprints, and acceleration sum were quantified per minute to compare the different games. Running distance in full match was greater than in 5vs5+GK (d = 2.303, moderate effect), 6vs6+GK (d = 1.719, moderate effect) and 9vs9+GK (d = 1.084, minimum effect) sided games. Greater values for sprinting distance were found in the full match compared to 5vs5+GK (d = 3.673, strong effect), 6vs6+GK (d = 2.606, moderate effect) and 9vs9+GK (d = 1.903, moderate effect) sided games. However, the load was greater in the 5vs5+GK game compared to the 6vs6+GK (d = 1.323, moderate effect) and 9vs9+GK (d = 1.030, minimum effect) games and the full match (d = 1.478, moderate effect). This study revealed that medium-sided games are not appropriate for simulating the sprinting conditions of official full matches. However, medium-sided games are more intense than full matches in that accelerations are made more often in medium-sided games.