Position Data Research Articles

Real-Time Bus Route and Timing Information System using IoT

This system suggests a user-friendly online interface for accessing real-time routes and schedules of the bus. The system provides the most recent information about bus locations, routes, and anticipated arrival times by utilizing the Internet of Things (IoT) and Global Positioning System (GPS) capabilities. The technology continuously collects position data by installing GPS modules on buses and linking them to an IoT network. After that, the data is sent to a central server for processing and display. The web interface serves as the main point of contact for users, it displays the positions of buses in real-time on a map along with integrated route data. By seeing the real-time position and expected arrival time of approaching buses, passengers may easily plan their journeys by accessing the webpage from any device with an internet connection. In addition to reducing wait times, enhancing customer satisfaction levels, and facilitating better travel arrangements, this gives transportation authorities useful information for optimizing bus routes and schedules.

Sensor placement determination for a wearable device in dual-arm manipulation tasks

To enhance the quality of life for individuals in various sectors, such as industry, healthcare, and everyday activities, the development of a highly efficient and cost-effective wearable device is essential. This paper presents a feasibility study for a general estimation methodology for human activity assessment using a single sensor placed on the back. The methodology considers the minimal variability in human body movements when carrying different weights. Our approach involves optimizing the sensor's position through a simple pre-processing technique. This technique utilizes virtual sensor position and orientation data derived from a virtual mannequin based on real inertial measurement unit (IMU) measurements obtained during two-arm manipulation tasks in a warehouse setting. The primary contribution is the optimization of a single, non-invasive sensor placement on the back to accurately assess activities involving two-hand manipulation with low variability in movement. We identified 54 different activity classes with an accuracy of 91.77% using a hybrid two-dimensional Convolutional Neural Network (CNN) combined with a Bidirectional Long Short-Term Memory (BiLSTM) network (2D CNN-BiLSTM) model. This model does not require additional sensors that measure other physical quantities, such as electromyography activity. Identifying repetitive tasks involving significant bending, stretching, and twisting, which pose risks of musculoskeletal disorders and back pain, offers a solution for designing wearable devices. The portability and autonomy of these devices are crucial, and current sensor technology meets these needs with low size, cost, and consumption. Wearable medical devices can thus be effectively used for self-monitoring health, preventive medicine, and rehabilitation.

FAIR ADCP data with OSADCP: a workflow to process ocean current data from vessel-mounted ADCPs

This paper presents the open-source Python software OSADCP developed for the processing of vessel-mounted Acoustic Doppler Current Profiler (VMADCP) data. At this stage, the toolbox is designed for processing VMADCP measurements from open-ocean applications of Teledyne RDI Ocean Surveyor ADCPs and the data acquisition software VMDAS. Based on the VMDAS ENX binary output format, the software contains implementations for cleaning and vector-averaging of single-ping velocity data, verification of the position data, and applying misalignment and amplitude corrections. The procedures of OSADCP are described in detail to encourage the scientific community to use it for their own purposes. The toolbox is an integral part of a workflow implemented on the German marine research vessels in the framework of the Underway Research Data project of the German Marine Research Alliance (DAM). It aims to ensure standardized data acquisition measures, reliable data transfer from the ADCP to shore both near-real-time and in delayed-mode, processing and quality control, and dissemination of the curated data product in the data repository PANGAEA. From PANGAEA, data sets are forwarded to the European marine data hubs Copernicus Marine Service and EMODnet. The workflow that forms the framework for OSADCP is described here as an example of scientific data management that follows the FAIR data guidelines.

Gait and Balance Assessments with Augmented Reality Glasses in People with Parkinson's Disease: Concurrent Validity and Test-Retest Reliability.

State-of-the-art augmented reality (AR) glasses record their 3D pose in space, enabling measurements and analyses of clinical gait and balance tests. This study's objective was to evaluate concurrent validity and test-retest reliability for common clinical gait and balance tests in people with Parkinson's disease: Five Times Sit To Stand (FTSTS) and Timed Up and Go (TUG) tests. Position and orientation data were collected in 22 participants with Parkinson's disease using HoloLens 2 and Magic Leap 2 AR glasses, from which test completion durations and durations of distinct sub-parts (e.g., sit to stand, turning) were derived and compared to reference systems and over test repetitions. Regarding concurrent validity, for both tests, an excellent between-systems agreement was found for position and orientation time series (ICC(C,1) > 0.933) and test completion durations (ICC(A,1) > 0.984). Between-systems agreement for FTSTS (sub-)durations were all excellent (ICC(A,1) > 0.921). TUG turning sub-durations were excellent (turn 1, ICC(A,1) = 0.913) and moderate (turn 2, ICC(A,1) = 0.589). Regarding test-retest reliability, the within-system test-retest variation in test completion times and sub-durations was always much greater than the between-systems variation, implying that (sub-)durations may be derived interchangeably from AR and reference system data. In conclusion, AR data are of sufficient quality to evaluate gait and balance aspects in people with Parkinson's disease, with valid quantification of test completion durations and sub-durations of distinct FTSTS and TUG sub-parts.

Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary Assessment

μEMA is a data collection method that prompts research participants with quick, answer-at-a-glance, single-multiple-choice self-report behavioral questions, thus enabling high-temporal-density self-report of up to four times per hour when implemented on a smartwatch. However, due to the small watch screen, μEMA is better used to select among 2 to 5 multiple-choice answers versus allowing the collection of open-ended responses. We introduce an alternative and novel form of micro-interaction self-report using speech input - audio-μEMA- where a short beep or vibration cues participants to verbally report their behavioral states, allowing for open-ended, temporally dense self-reports. We conducted a one-hour usability study followed by a within-subject, 6-day to 21-day free-living feasibility study in which participants self-reported their physical activities and postures once every 2 to 5 minutes. We qualitatively explored the usability of the system and identified factors impacting the response rates of this data collection method. Despite being interrupted 12 to 20 times per hour, participants in the free-living study were highly engaged with the system, with an average response rate of 67.7% for audio-μEMA for up to 14 days. We discuss the factors that impacted feasibility; some implementation, methodological, and participant challenges we observed; and important considerations relevant to deploying audio-μEMA in real-time activity recognition systems.

A basketball paradox: exploring NBA team defensive efficiency in a positionless game

Abstract In the last decade, the offensive and defensive philosophies employed by teams in the National Basketball Association (NBA) have changed substantially. As a result, most players can no longer be classified into only one of the five traditional positions (PG, SG, SF, PF, C) and instead spend a percentage of their playing time at multiple positions, making positional data compositional. Further, given the desirability for versatile players, an argument can be made that traditional positions themselves are archaic. Using data from the 2016–17, 2017–18, and 2018–19 seasons, I explore how Bayesian hierarchical models can be used to estimate team defensive strength in three ways. First, only considering players classified by their majority traditional position. Second, by using compositional traditional positional data. Third, using compositional data from modern positions (archetypes) defined by fuzzy k-means clustering. I find that the fuzzy k-means approach leads to a modest improvement in both the root mean squared error and median 95 % posterior predictive interval width for the test data, and, more importantly, identifies 11 modern archetypes that, when combined, are correlated with team win total and adjusted team defensive rating. The modern archetype compositions can be used by stakeholders to better understand team defensive strength.

Utilizing Augmented Reality to Evaluate Service Member Team Performance.

Decision-making is a complex process that relies on situational awareness and experience to create a potential list of actions while weighing the risks and benefits of each action. There is a paucity of data evaluating decision-making for individual service members (SM) during the performance of team-based military-relevant activities. Understanding individual performance and decision-making within the context of a team-based activity has potential to aid in the detection and management of mild traumatic brain injuries and assist with safe and timely return-to-duty decision making. The aim of this project was to evaluate cognitive and motor performance in healthy SM during an augmented reality military specific, team-based activity. Data from 110 SMs from Fort Moore Georgia were analyzed for this project. Service members completed 3 augmented reality room breaching and clearing scenarios (Empty Room, Civilian/Combatant, and Incorrect Position of a unit member) with 3 avatar team members. Participants wore a Microsoft HoloLens 2 (HL2) device and used a replica M4 weapon (Haptech Defense Systems) during scenarios. Three-dimensional position data from the HL2 headset was used to compute temporal measures of room breaching and clearing events while the number and timing of weapon discharge was monitored by the M4. Temporal outcomes included time to enter room, time to fire first shot, time in fatal funnel, and total trial time while motor outcomes were distance traveled and average movement velocity. Pairwise comparisons between the Incorrect Position scenario and the Civilian/Combatant scenario demonstrated no difference in time to enter the room (2.36 seconds in both scenarios). Time to fire the first shot in the Civilian/Combatant scenario was longer (0.97 seconds to 0.58 seconds) while time in fatal funnel (2.58 seconds to 3.31 seconds) and time to trial completion (7.46 seconds to 8.41 seconds) were significantly longer in the Incorrect Position scenario. Reaction time to fire the first shot, time in the fatal funnel, and total trial time reflect a change in information-processing and decision-making capabilities during military specific, ecological, team-based scenarios when altering the environment inside of the room and modifying avatar movements. Future studies are planned to evaluate the effects of mild traumatic brain injury on specific aspects of military team performance.

High-Precision Positioning and Rotation Angle Estimation for a Target Pallet Based on BeiDou Navigation Satellite System and Vision.

In outdoor unmanned forklift unloading scenarios, pallet detection and localization face challenges posed by uncontrollable lighting conditions. Furthermore, the stacking and close arrangement of pallets also increase the difficulty of positioning a target pallet. To solve these problems, a method for high-precision positioning and rotation angle estimation for a target pallet using the BeiDou Navigation Satellite System (BDS) and vision is proposed. Deep dual-resolution networks (DDRNets) are used to segment the pallet from depth images and RGB images. Then, keypoints for calculating the position and rotation angle are extracted and further combined with the 3D point cloud data to achieve accurate pallet positioning. Constraining the pixel coordinates and depth coordinates of the center point of the pallet and setting the priority of the pallet according to the unloading direction allow the target pallet to be identified from multiple pallets. The positioning of the target pallet in the forklift navigation coordinate system is achieved by integrating BDS positioning data through coordinate transformation. This method is robust in response to lighting influences and can accurately locate the target pallet. The experimental results show that the pallet positioning error is less than 20 mm, and the rotation angle error is less than 0.37°, which meets the accuracy requirements for automated forklift operations.

Precise Positioning in Nitrogen Fertility Sensing in Maize (Zea mays L.).

This study documented the contribution of precise positioning involving a global navigation satellite system (GNSS) and a real-time kinematic (RTK) system in unmanned aerial vehicle (UAV) photogrammetry, particularly for establishing the coordinate data of ground control points (GCPs). Without augmentation, GNSS positioning solutions are inaccurate and pose a high degree of uncertainty if such data are used in UAV data processing for mapping. The evaluation included a comparative assessment of sample coordinates involving RTK and an ordinary GPS device and the application of precise GCP data for UAV photogrammetry in field crop research, monitoring nitrogen deficiency stress in maize. This study confirmed the superior performance of the RTK system in providing positional data, with 4 cm bias as compared to 311 cm with the non-augmented GNSS technique, making it suitable for use in agronomic research involving row crops. Precise GCP data in this study allow the UAV-based Normalized Difference Red-Edge Index (NDRE) data to effectively characterize maize crop responses to N nutrition during the growing season, with detailed analyses revealing the causal relationship in that a compromised optimum canopy chlorophyll content under limiting nitrogen environment was the reason for reduced canopy cover under an N-deficiency environment. Without RTK-based GCPs, different and, to some degree, misleading results were evident, and therefore, this study warrants the requirement of precise GCP data for scientific research investigations attempting to use UAV photogrammetry for agronomic field crop study.

Dynamic and probabilistic safety zones for autonomous mobile robots operating near humans

The inefficiency of maintaining static and long-lasting safety zones in environments where actual risks are limited is likely to increase in the coming decades, as autonomous systems become more common and human workers fewer in numbers. Nevertheless, an uncompromising approach to safety remains paramount, requiring the introduction of novel methods that are simultaneously more flexible and capable of delivering the same level of protection against potentially hazardous situations. We present such a method to create dynamic safety zones, the boundaries of which can be redrawn in real-time, taking into account explicit positioning data when available and using conservative extrapolation from last known location when information is missing or unreliable. Simulation and statistical methods were used to investigate performance gains compared to static safety zones. The use of a more advanced probabilistic framework to further improve flexibility is also discussed, although its implementation would not offer the same level of protection and is currently not recommended.

Adaptive approach for tracking movements of biological targets: application to robot-based intervention for prostate cancer.

In this paper, we introduce an advanced robotic system integrated with an adaptive optimization algorithm, tailored for Brachytherapy in prostate cancer treatment. The primary innovation of the system is the algorithm itself, designed to dynamically adjust needle trajectories in response to the real-time movements of the prostate gland during the local intervention. The system employs real-time position data extracted from Magnetic Resonance Imaging (MRI) to ensure precise targeting of the prostate, adapting to its constant motion and deformation. This precision is crucial in Brachytherapy, where the accurate placement of radioactive seeds directly impacts the efficacy of the treatment and minimizes damage to surrounding safe tissues. Our results demonstrate a marked improvement in the accuracy of radiation seed placement, directly correlating to more effective radiation delivery. The adaptive nature of the algorithm significantly reduces the number of needle insertions, leading to a less invasive treatment experience for patients. This reduction in needle insertions also contributes to lower risks of infection and shorter recovery times. This novel robotic system, enhanced by the adaptive optimization algorithm, improves the coverage of targets reached by a traditional combinatorial approach by approximately 15% with fewer required needles. The improved precision and reduced invasiveness highlight the potential of this system to enhance the overall effectiveness and patient experience in prostate cancer Brachytherapy.

Has Reducing Ship Emissions Brought Forward Global Warming?

AbstractShips brighten low marine clouds from emissions of sulfur and aerosols, resulting in visible “ship tracks”. In 2020, new shipping regulations mandated an ∼80% reduction in the allowed fuel sulfur content. Recent observations indicate that visible ship tracks have decreased. Model simulations indicate that since 2020 shipping regulations have induced a net radiative forcing of +0.12 Wm−2. Analysis of recent temperature anomalies indicates Northern Hemisphere surface temperature anomalies in 2022–2023 are correlated with observed cloud radiative forcing and the cloud radiative forcing is spatially correlated with the simulated radiative forcing from the 2020 shipping emission changes. Shipping emissions changes could be accelerating global warming. To better constrain these estimates, better access to ship position data and understanding of ship aerosol emissions are needed. Understanding the risks and benefits of emissions reductions and the difficultly in robust attribution highlights the large uncertainty in attributing proposed deliberate climate intervention.

Integration of attention mechanism and CNN-BiGRU for TDOA/FDOA collaborative mobile underwater multi-scene localization algorithm

The aim of this study is to address the issue of TDOA/FDOA measurement accuracy in complex underwater environments, which is affected by multipath effects and variations in water sound velocity induced by the challenging nature of the underwater environment. To this end, a novel cooperative localisation algorithm has been developed, integrating the attention mechanism and convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) with TDOA/FDOA and two-step weighted least squares (ImTSWLS). This algorithm is designed to enhance the accuracy of TDOA/FDOA measurements in complex underwater environments. The algorithm initially makes use of the considerable capacity of a convolutional neural network (CNN) to extract profound spatial and frequency domain characteristics from multimodal data. These features are of paramount importance for the characterisation of underwater signal propagation, particularly in complex environments. Subsequently, through the use of a bidirectional gated recurrent unit (BiGRU), the algorithm is able to effectively capture long-term dependencies in time series data. This enables a more comprehensive analysis and understanding of the changing pattern of signals over time. Furthermore, the incorporation of an attention mechanism within the algorithm enables the model to focus more on the signal features that have a significant impact on localisation, while simultaneously suppressing the interference of extraneous information. This further enhances the efficiency of identifying and utilising the key signal features. ImTSWLS is employed to resolve the position and velocity data following the acquisition of the predicted TDOA/FDOA, thereby enabling the accurate estimation of the position and velocity of the mobile radiation source. The algorithm was subjected to a series of tests in a variety of simulated underwater environments, including different sea states, target motion speeds and base station configurations. The experimental results demonstrate that the algorithm exhibits a deviation of only 2.88 m/s in velocity estimation and 2.58 m in position estimation when the noise level is 20 dB. The algorithm presented in this paper demonstrates superior performance in both position and velocity estimation compared to other algorithms.

Drive away neck pain: Transformative motor control therapeutic neck exercises and tailored programs redefine posture for automotive workers.

Neck discomfort, arising from strained muscles, poor posture, accidents, or underlying medical conditions, significantly impacts daily activities and overall well-being, presenting a substantial healthcare challenge. This study aims to evaluate the efficacy of motor control therapeutic neck exercise and a Structured Exercise Program on neck pain and posture among automobile industrial workers. Using a lottery technique, 106 participants aged 20 to 45 were selected from the vicinity of Chennai's auto industries based on predetermined eligibility and exclusion criteria. Subsequently, participants were randomly assigned to either the motor control therapeutic neck exercise group or the structured exercise program group, each comprising 53 individuals. Participants received a comprehensive overview of the study and respective interventions, and their informed consent was obtained. Demographic details, Neck Injury and Disability scores, and Assessment of Postural and Ergonomic Characteristics data were then collected. Statistical analysis revealed a significant difference between the groups, with the Motor Control Therapeutic Neck Exercise group demonstrating superior outcomes in reducing pain and improving posture, with p-values less than 0.001. This study highlights the efficacy of Motor Control Therapeutic Neck Exercise over Structured Exercise in improving neck pain and posture among automobile industrial workers. These findings contribute valuable insights for the development of targeted interventions in occupational health settings.

Adaptive Frame Structure Design for Sensing-Assisted Downlink Communication in the Vehicle-to-Infrastructure Scenario.

Vehicle-to-everything (V2X) is considered a key factor in driving the future development of intelligent transport, which requires high-quality communication and fast sensing of vehicle information in high-speed mobile scenarios. However, high-speed mobility makes the wireless channel change rapidly, which requires frequent channel estimation and channel feedback between a vehicle and the roadside unit (RSU), resulting in an increase in communication overhead. At the same time, the high maneuverability of vehicles leads to frequent switching and misalignment of communication beams, so the RSU must have better beam prediction and tracking capabilities. To address this problem, this paper proposes an adaptive frame structure design scheme for sensing-assisted downlink (DL) communication. The basic idea of the scheme involves analyzing the communication model during the vehicle's movement. This analysis aims to establish a theoretical relationship between the Symbol Error Rate (SER) and the following two key factors: the vehicle's starting position and the distance it travels across. Subsequently, the scheme leverages the vehicle's position data, as detected by the RSU, to calculate the real-time SER for DL communication. The SER threshold is set based on the requirements of DL communication. If the real-time SER is below this threshold, channel estimation becomes unnecessary. This decreases the frequency of channel estimation and frees up time and frequency resources that would otherwise be occupied by channel estimation processes within the frame structure. The design of an adaptive frame structure, as detailed in the above scheme, is presented. Furthermore, the performance of the proposed method is analyzed and compared with that of the traditional communication protocol frame structure and the beam prediction-based frame structure. The simulation results indicate that the communication throughput of the proposed method can be improved by up to 6% compared with the traditional communication protocol frame structure while maintaining SER performance.

Analysis of the dynamic performance of pantograph-catenary system with posture of pan-head

Abstract This article focuses on the changes in the contact pressure between the pantograph and the catenary caused by variations in the pan-head posture. Starting from the kinematics and dynamics of the pantograph and based on the theories of multibody dynamics and finite element analysis, the pantograph head model is validated, and a pantograph rigid-flexible coupling model considering the flexibility of the pantograph head is established. A coupling model between the pantograph model and the rigid contact catenary is established to extract the pantograph posture and dynamic performance data during operation. By finely considering and modeling the structural characteristic parameters of the pantograph, the influence of different pantograph head characteristic parameters on the dynamic performance of the catenary system is studied. This provides a research basis for understanding the degree of influence of different characteristic parameters on the catenary dynamics.

Research on rescue priority based on high spatiotemporal resolution mobile positioning data

Estimate the key rescue areas of earthquake accurately, which is of great significance for deploying rescue forces and implementing rescue activities in post-earthquake scientifically.This paper based on the idea of first zoning, then classification, and then prioritizing rescue, taking the core area of Tangshan City as the study area, based on urban road data and mobile positioning data, combined with GIS methods to achieve street level rescue zoning, k-means clustering analysis is used to classify rescue sectors, and personnel burial model is used to conduct rescue priority classification.The results indicate that rescue priority is closely related to the time of earthquake occurrence. When the earthquake occurs between 18pm and 7pm in the next day, the number of priority rescue sector at level I and II is the highest. When the earthquake occurs between 8am and 11am on weekends, the number of priority rescue sector in residential areas increases, while the number of priority rescue zone decreases in workspace areas. This study provides refined rescue zoning and priority grading in the early stages of disaster relief with the absence of disaster information, which will help to assist in decision-making for professional force dispatch.

A Novel Unsupervised Machine Learning Approach to Assess Postural Dynamics in Euthymic Bipolar Disorder.

Bipolar disorder (BD) is a mood disorder with different phases alternating between euthymia, manic or hypomanic episodes, and depressive episodes. While motor abnormalities are commonly seen during depressive or manic episodes, not much attention has been paid to postural abnormalities during periods of euthymia and their association with illness burden. We collected 24-hour posture data in 32 euthymic participants diagnosed with BD using a shirt-based wearable. We extracted a set of nine time-domain features, and performed unsupervised participant clustering. We investigated the association between posture variables and 12 clinical characteristics of illness burden. Based on their postural dynamics during the daytime, evening, or nighttime, participants clustered in three clusters. Higher illness burden was associated with lower postural variability, in particular during daytime. Participants who exhibited a mostly upright sitting/standing posture during the night with frequent nighttime postural transitions had the highest number of lifetime depressive episodes. Euthymic participants with BD exhibit postural abnormalities that are associated with illness burden, especially with the number of depressive episodes. Our results contribute to understanding the role of illness burden on posture changes and sleep consolidation in periods of euthymia.

GIS and GNSS Integration Techniques for Earth Observations

Abstract: The term "GNSS," or global navigation satellite system, refers to any satellite navigation system with global coverage. To determine your current location, GNSS devices and receivers employ geolocation information from satellite navigation systems. Trilateration is the basis for how navigation satellites operate. An object's position on the spheroid is defined by its latitude, longitude, and altitude above mean sea level. Today, the utilisation of Global Navigation Satellite Systems (GNSS) technology is required for a wide range of uses, including engineering, cities, and defence. Geographic Information Systems (GIS) can be integrated with GPS to create powerful tools for mapping and spatial analysis. This integration allows for precise display of geographic characteristics on maps, land use planning, response to emergencies, and management of natural resources. Precision agriculture is one area where GNSS and GIS are integrated, enabling farmers to gather precise position data while operating machinery. GNSS and GIS integration can be done through position-focused and technology-focused integration. Position-focused integration uses GNSS technology to locate items or features on the earth's topography, while technology-focused integration enhances the utility and accuracy of GNSS data using GIS applications and technology. Both forms of integration provide potent solutions for various industries.

The Finishing Space Value for Shooting Decision-Making in High-Performance Football.

Football players' decision-making behaviours near the scoring target (finishing situations) emerge from the evolving spatiotemporal information directly perceived in the game's landscape. In finishing situations, the ball carrier's decision-making about shooting or passing is not an individual decision-making process, but a collective decision that is guided by players' perceptions of match affordances. To sustain this idea, we collected spatiotemporal information and built a model to quantify the "Finishing Space Value" (FSV) that results from players' perceived affordances about two main questions: (a) is the opponent's target successfully reachable from a given pitch location?; and (b) from each given pitch location, the opposition context will allow enough space to shoot (low adversaries' interference)? The FSV was calculated with positional data from high-performance football matches, combining information extracted from Voronoi diagrams (VD) with distances and angles to the goal line. FSV was tested using as a reference the opinion of a "panel of expert" (PE), composed by football coaches, about a questionnaire presenting 50 finishing situations. Results showed a strong association between the subjective perception scale used by the PE to assess how probable a shot made by the ball carrier could result in a goal and FSV calculated for that same situation (R2=0.6706). Moreover, we demonstrate the accuracy of the FSV quantification model in predicting coaches' opinions about what should be the "best option" to finish the play. Overall, results indicated that the FSV is a promising model to capture the affordances of the shooting circumstances for the ball carrier's decision-making in high-performance football. FSV might be useful for more precise match analysis and informing coaches in the design of representative practice tasks.