Tracking Applications Research Articles

A Low-Cost Smart Wearable Glove for Non-Invasive Health Monitoring

Wearable physiological signal monitoring systems hold notable potential in the future of personal healthcare by seamlessly integrating into daily life, providing continuous monitoring, and aiding in the early detection of health issues. This research presents a wearable health monitoring glove, with a focus on cost-effectiveness while maintaining efficiency for developing countries. The wearable glove can track vital physiological indicators like Blood Pressure, Body Temperature, Glucose level, Blood Oxygen Saturation, Hemoglobin level, ECG, Room Temperature, Humidity, and Motion Tracking. A user-friendly interface facilitates easy interaction, while efforts in energy-efficient design and power management aim to prolong battery life. Also, real-time data monitoring ensures precision in signal analysis and the extraction of vital health data of individuals. The proposed wearable hand glove utilizes a collection of sensors and integrates them towards the diverse detection of skin humidity, temperature, blood oxygen, hemoglobin, etc. non-invasively. Apart from its technical features, the research explores potential applications in healthcare, fitness tracking, and research fields, presenting a versatile solution. Beyond its technical attributes, the research explores potential applications in medical and personal healthcare, fitness tracking, sports, etc. Collaborative efforts with regulatory bodies assess the feasibility of obtaining necessary approvals or certifications, while scalability considerations pave the way for potential mass production and market deployment.

Smart Ambulance Service and Tracking Applications for Critical Needs

This abstract introduces a Smart Ambulance Service (SAS) along with its accompanying service application designed to address critical needs in emergency medical care. The SAS incorporates cutting-edge technologies such as Internet of Things (IoT) and real-time data analytics to enhance the efficiency and effectiveness of emergency response systems. The SAS aims to revolutionize traditional ambulance services by leveraging IoT devices installed within ambulances. These devices collect vital signs of patients en route to the hospital, such as heart rate, blood pressure, and oxygen levels, and transmit this data in real-time to healthcare providers. This real-time data transmission allows medical personnel to prepare for the patients arrival, making the transition from ambulance to hospital smoother and reducing delays in treatment. Furthermore, the service application is user-friendly interface accessible to both emergency responders and hospital staff. It provides a centralized platform for managing ambulance dispatch, tracking ambulance locations and monitoring patient conditions remotely. Emergency responders can input patient information, vital signs, and the nature of the emergency directly into the application, ensuring that hospital staffs are well- informed before the ambulance arrives.

Application of bolus tracking: The effect of ROI positions on the images quality of cervicocerebral CT angiography

BackgroundCervicocerebral CT angiography (CTA) using the bolus tracking technique has been widely used for the assessment of cerebrovascular diseases. Regions of interest (ROI) can be placed in the descending aorta, ascending aorta, and the aortic arch. However, no study has compared the arteries and veins display when when the region of interest (ROI) is placed at different sites. In this study, we showed the impact of ROI positions on the image quality of cervicocerebral CTA. MethodsTwo hundred and seventy patients who underwent cervicocerebral CTA with bolus tracking technique were randomly divided into three groups based on the position of the ROI placement: ascending aorta (Group 1, n = 90), aortic arch (Group 2, n = 90), and descending aorta (Group 3, n = 90). The scanning parameters and contrast agent injection protocols were consistent across all groups. Three observers independently assessed the objective image quality, while two observers jointly assessed the subjective image quality using a grade scale: poor (grade 1), average (grade 2), good (grade 3), and excellent (grade 4). The differences in intravascular CT values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), AVCR (arterial venous contrast ratio), and subjective image quality scores were compared among the three groups. ResultsThe CT values of the intracranial veins (superior sagittal sinus, ethmoid sinus and great cerebral vein) in group 1 were significantly lower than those in group 3 (p < 0.001). However, no significant differences were observed in CT values, SNR and CNR in the internal carotid artery and middle cerebral artery among the three groups. The proportion of images with grade 4 was significantly higher in group 1 than group 2 and 3 (41.1% vs 15.6% and 13.3%, p < 0.001). The proportion of images with grade 1 was significantly lower in group 1 than group 2 and 3 (1.1% vs 6.6% and 17.8%, p < 0.001). ConclusionThe ROI positions for cervicocerebral CTA did not affect the arterial image quality, but venous structures imaging was affected when the ROI was placed in the ascending aorta.

Ultra-fast tuning of neural network controllers with application in path tracking of autonomous vehicle

Neural network (NN) controllers have shown great potential in solving complex control or decision-making tasks. However, most of the NN controllers either rely on the availability of large datasets or require dense interactions with the environment, which hinders their application in real systems. In this paper, we introduce a model-based reinforcement learning (MBRL) algorithm, aimed at realizing ultra-fast tuning of deep NN controller from a small sample set of real-world data. The algorithm uses Gaussian processes (GPs) to model the unknown dynamics of real system and updates controller parameters through stochastic gradient descent. By using particle-based method for long-term predictions, the algorithm can easily incorporate online state estimators and filters into controller learning, which is conductive to learning from systems with partially measurable states and stochastic control delay. We apply the algorithm to calibrate a deep NN controller for the path tracking of a full-size autonomous vehicle (AV). Simulation and field test results show that the deep NN controller can be well calibrated after only one interaction with the environment and can achieve similar tracking performance to optimization-based methods such as nonlinear model prediction control (NMPC) in various test scenarios by combining with a feed-forward pure pursuit (PP) controller.

Image quality evaluation: evaluation of the image quality of actual images by using machine learning models

Evaluating image features is a significant step in image processing in applications like number plate detection, vehicle tracking and many image processing-based applications. Image processing-based applications need accurate parts to get the best outcomes. Feature detection is done based on various feature detection techniques. The proposed system aims to get the best feature detector based on the input images by evaluating the image features. For assessing the image features, the proposed system worked on various descriptors like oriented FAST and rotated brief (ORB), learned arrangements of three patch codes (LATCH), binary robust independent elementary features (BRIEF), and binary robust invariant scalable keypoints (BRISK) to extract and evaluate the features using K-nearest neighbor (KNN)-matching and retrieve the inliers of the matching. Each descriptor produces different matching features and inliers; with the matchings and inliers, the inlier ratio calculates to show the analysis. To increase performance, we also examine adding depth information to descriptors.

210 Defining the Learning Curve for Cerebral Angiography Using a Novel Web-Based Feedback Application and Head-Mounted Video Review

INTRODUCTION: The milestones that a resident or fellow should reach to achieve competence in cerebral angiography have yet to be defined. METHODS: Two neurosurgical residents were given a written knowledge exam before and after rotating on the endovascular service. Cerebral angiography diagnostic and treatment cases were prospectively collected in a novel web-based, multi-institutional feedback application. These residents performed cerebral angiography cases under graduated supervision while wearing head-mounted cameras for subsequent review with direct feedback. Residents and reviewers independently utilized CaseCurve, a novel neurosurgical resident performance tracking application, as a platform for performance rating. Numerical scores were assigned ranging from 1 (novice) to 5 (masterful) in the following categories: access, catheter navigation, table operation, closure, efficiency, finesse, angiographic interpretation, and catheter knowledge. The number of times the supervising surgeon had to take over the case was recorded. Arch anatomy and vessel tortuosity were graded. Operative videos were analyzed for operator inefficiencies and time metrics for sheath insertion and target vessel selection. Angiographic efficiency was assessed with fluoroscopic doses/times and procedural durations. RESULTS: Both residents demonstrated improvement in cognitive performance as measured by written assessment scores. Significant improvements were observed in technical performance as measured by numerical scores assigned by self-evaluation, faculty review, objective measures of angiographic efficiency, and the number of times the supervising surgeon had to take over the procedure. Concordance between faculty and resident self-evaluations was 74.2%, with residents underestimating their performance in 21% and overestimating in 5%. CONCLUSIONS: There is significant educational value for the trainer and trainee offered by our novel web-based feedback application and head-mounted video review. This data allows for accurate assessments of skill acquisition and delineation of appropriate milestones/learning categories for trainees in endovascular neurosurgery.

Predicting trail condition using random forest models in urban-proximate nature reserves

Monitoring and managing the condition of recreational trails is a time- and resource- intensive process often requiring significant field data. We developed a method for predicting trail condition over 183 km of trails in three urban-proximate nature reserves in Orange County, California using field data from 118 km of trails and random forest (RF) models. Further, we use data from the fitness tracking application Strava to measure recreation use intensity, activity type, and spatial dimensions of visitor use. Our results indicate 30 km of trails in two nature reserves that are at risk of significant trail degradation. Additionally, trail grade, NDVI, and use-related factors such as activity type and use intensity were ranked among the most important variables for predicting trail condition. Variable importance measures produced by RF models can help inform site-specific trail management that takes environmental, managerial, and use-related factors into account. We argue that RF models, in combination with Strava data, are powerful tools for outdoor recreation monitoring and management.

Robotic welding system for adaptive process control in gas metal arc welding

Changing process conditions such as distortion, varying seam preparation or gap width during welding is a major challenge in automated gas metal arc welding (GMAW). While human welders can adjust the process during welding (e.g. welding speed, torch orientation), an automated welding system needs sensors to detect and actuators to adjust the process. Adjusting the process in response to changing process conditions is usually referred to as adaptive welding. The aim of this work is to build a robotic welding system capable of automatically adapting the welding process using some of the approaches of a human welder. To enable adaptive process control, a robotic welding system is built. It consists of four main components: a six-axis industrial robot for mechanical guidance of the welding torch, a welding power source, a monochrome visual camera as an image sensor and a process controller that combines the three components. The camera captures images of the weld pool during welding and processes the images to provide geometrical information such as the width of the weld pool and the position of the weld pool front. Changes in the weld pool geometry are quantified, and an adjustment strategy is generated in the process control unit in real time. Process adjustments can be mechanical (e.g. welding speed, torch orientation) and electrical by adjusting synergic process settings (wire feed speed, arc length, process dynamics). Validation tests demonstrate the functionality of the welding system. Two use cases were investigated. Firstly, a deposited weld bead was examined, and variations in the width of the weld pool were induced by varying the welding speed. The second application was a seam tracking application. The path is pre-programmed, and the specimen is positioned with an offset to the path. Compensation for the offset is implemented.

Performance Evaluation of Cardiac Signal Recording Framework (CARDIF)-A Quantitative Assessment for Long Term Monitoring Applications

Background: Personal health monitoring with wearable electronics has gained momentum in the recent years due to its usage in flexible textile-based sensors/electrodes for recording of physiological vital parameters. Such system provides scope for long term ambulatory ECG measurement and motion tracking applications. The selection /design of textile sensors play a vital role as it has to overcome skin irritations, improve the skin-electrode impedance required for conductivity. The composition of textile materials, shape and size of the textile electrodes contributes significantly towards the conductivity. Objective: To assess the proposed Cardif system for its suitability to introduce in the clinical routine Method: The proposed textile electrodes were designed using knit jersey conductive material. The skin –contact impedance of the proposed textile material, was measured using two electrode impedance method and measurement was done for different age groups by varying the frequency. Results: The measurement results showed that the impedance was decreasing with increasing frequency and was found to be below 1.5Mohm/cm2 in the frequency range of 20Hz to 1KHz for three different age groups. The performance of the CARDIF was assessed using heart rate, RR interval, SNR as well as qualitative assessment through visual inspection and were compared with gel based disposable Ag/AgCl electrodes. Qualitative and quantitative analysis was performed and the various results confirm the proposed textile electrodes for continuous patient monitoring applications.

An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance

A pulse-Doppler (PD) radar has the advantage of strong anti-interference ability, and it is often used as a solution for maneuvering target tracking. In the application of target monitoring and tracking in PD radars, the interacting multiple model algorithm (IMM) has become the main and preferred choice due to its flexibility and high accuracy. However, the probability transfer matrix in classical IMM algorithms generally depends on constant prior knowledge, and if a PD radar is tracking a strong maneuvering target, it is inevitable to encounter some limitations, such as the possibility of target tracking trajectory deviation, and even a loss of the target. The Markov probability transfer matrix is proposed with an adaptive modification ability in real time to overcome the above problems in this paper. Additionally, for improving the speed of switching between the models, the fuzzy control system for secondary updating of model probability is adopted. By this means, the tracking accuracy of maneuvering targets is enhanced. Compared with the classical IMM algorithm, the corresponding simulation results for the PD radar indicate that the overall tracking accuracy of the proposed adaptive IMM algorithm is improved by 19.6%. In conclusion, the continuity and accuracy of the target trajectory can be effectively improved with the proposed adaptive IMM algorithm in PD radar cases.

Fixed-structure parameter-dependent state feedback controller: A scaled autonomous vehicle path-tracking application

This paper introduces theoretical conditions for the computation of a novel type of state-feedback controller that makes use of Linear Parameter Varying approaches for synthesis. The novelty of this new State-Feedback controller lies on the fact that the controller has a fixed structure with constants matrix gains. However, the controller gains are affine on a parameter dependent basis function, which allows the controller to self-schedule according to real-time changes of the varying parameters. This type of controller is conceived with a focus on implementation, as in contrast with most other LPV approaches. The implementation of this Parameter-Dependent State-Feedback controller does not require any online interpolation or matrix inverse operations, independently of the number of varying parameters. The performance of the proposed approach is validated in a Scaled Autonomous Vehicle for steering control in a path tracking application.

(190) Digital Contraception: Effectiveness, Data Privacy, Reproductives Rights

Abstract Introduction We are experiencing a new profound societal transformation: the contraceptive crisis, attributed to the side-effects of hormonal contraception (since 2012) meets the rise of digital technologies. People seem to reject even the slightest risk of hormonal contraception, in favor of entrusting their intimitate data to online companies. But is the rejection of hormonal contraception really du to its iatrogenic risks? Hormonal contraception is 60 years old and its risks are not new and already known, and people don't hesitate to take far greater risks in all areas of their lives: sexual practices and encounters with strangers, sports activities, driving motor vehicles, tobacco, alcohol, etc... In view of this paradox, we will attempt to assess the advantages of digital contraception over medical contraception. Objective Drawing up an inventory of scientific research on digital contraception Know the real reliability of digital contraception and menstruations trackers Know the real risks for health and the other risks of these trackers Using the information obtained, be able to communicate and inform users as objectively as possible Methods Literature review on PubMed and Google Scholar for the last 12 months with the keywords: digital contraception, menstruations applications, period tracking applications, menstruations applications and data priv0acy. Results PubMed reports only six relevant studies about digital period trackers: these are concerned solely with the prospect of pregnancy or with the interest of data that might suggest a pathology such as Polycystic ovary syndrome, endometriosis or some other conditions. One study report that further research is needed to assess contents and its use. It is interesting to note that "data privacy" gives here no result, no result for "digital contraception" either. "Digital contraception" is still not a buzzword on Google Scholar" with only one relevant survey, and various research studies on period trackers report a lack of reliability in informations purporting to be scientific. What's most worrying is that the period trackers use external services for data storage with no guarantee of data protection. Literature reports that users are falsely reassured about their privacy by a non-human actor and are not aware that their data can be sold to mercantile companies. Little known consequences are also about ecology with the pollution of the data centers and about reproductive rights by monitoring periods of amenorrhea. Conclusions There are many blinds spots for users when it comes to menstruations trackers, and it is surprising to see people trusting commercial device, about their health, without any independent scientific guarantee. Thus, we observe a loss of trust for health care providers and we have to understand the current situation in sexual and reproductive health: May be we have to ask not why don't people use medical contraception, but why do people use medical contraception? What are the key factors of using a medical contraception? Rejection of medical authority or even gynecological violence, new gender relations, sexual satisfaction, accessibility of medical contraception must be assessed to place users at the center of the care system. Disclosure No.

Real-Time Optical Localization and Tracking of UAV Using Ellipse Detection

A real-time three-dimensional (3D) spatial localization system to track a UAV (unmanned aerial vehicle) using a single off-board conventional camera and an onboard circle-shaped colored Light-Emitting Diode (LED) marker is presented. The image from the camera is color-segmented and then morphologically closed before being sent to the ellipse detector. The location of the drone is then estimated with geometric/optical calculations using the pixel coordinates provided by the real-time ellipse detector. The location data generated by this system was validated by a Motion-Capture System (MCS) that was simultaneously tracking the system in real-time. The average position error was of 6cm, while the processing speed achieved 15 locations/second, which are comparable with recent research references. The usage of a low-cost camera makes this method promising for most mobile UAV tracking applications.

Efficient Campus Shuttle Tracking and Management Mobile Application for College Campus

Efficient Campus Shuttle Tracking and Management Mobile Application for College Campus

The Use of Eye-Tracking Technology in Dento-Maxillofacial Esthetics: A Systematic Review.

The use of eye-tracking technology in dental esthetics has gained popularity over the past decade because of its ability to assess observers' visual preferences in an objective manner. The goal of this study was to provide a comprehensive review of eye-tracking studies in dentistry, which could provide a reference for the rational and effective application of eye-tracking technology by dentists in the future. A comprehensive search of articles on eye tracking, published from January 1946 to June 2023, was conducted across several databases using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The major criterion for inclusion was that the study evaluated the use of eye-tracking technology in the field of dentistry. Two independent reviewers screened the eligible studies. A total of 67 articles were identified, 41 of which met our inclusion criteria. The most common application of eye tracking was the assessment of perceptions of changes in specific dental conditions among different classes of observers. Overall differences between groups (different classes of observers, different types of conditions) among different areas or regions of interest were analyzed. This systematic review demonstrated the utility of eye-tracking technology as a quantifiable objective assessment and emerging research tool for evaluating outcomes in several domains of dentistry.

An Iteratively Extended Target Tracking by Using Decorrelated Unbiased Conversion of Nonlinear Measurements.

Extended target tracking (ETT) based on random matrices typically assumes that the measurement model is linear. However, nonlinear measurements (such as range and azimuth) depending on locations of a series of unknown scattering centers always exist in many practical tracking applications. To address this issue, this paper proposes an iteratively extended target tracking based on random matrices by using decorrelated unbiased conversion of nonlinear measurements (ETT-IDUCM). First, we utilize a decorrelated unbiased converted measurement (DUCM) method to convert nonlinear measurements depending on unknown scatters of target extent in polar coordinates into the ones in Cartesian coordinates with equivalent measurement noise covariances. Subsequently, a novel method, combining iteratively extended Kalman filter (IEKF) updates with variational Bayesian (VB) cycles is developed for precise estimation of the target's kinematic state and extension. This method leverages the synergy between external IEKF iterations, which use the estimated state as a new prediction and input for DUCM, and internal VB iterations, which realize a closed-form approximation of the joint posterior probability. This approach progressively enhances estimation accuracy. Simulation results demonstrate the ETT-IDUCM algorithm's superior precision in estimating the target's kinematic state and extension compared to existing methods.

Optimising wound monitoring: Can digital tools improve healing outcomes and clinic efficiency.

Chronic wounds present significant challenges for patients and nursing care teams worldwide. Digital health tools offer potential for more standardised and efficient nursing care pathways but require further rigorous evaluation. This retrospective matched cohort study aimed to compare the impacts of a digital tracking application for wound documentation versus traditional manual nursing assessments. Data from 5236 patients with various wound types were analysed. Propensity score matching balanced groups, and bivariate tests, correlation analyses, linear regression, and Hayes' Process Macro Model 15 were utilised for a mediation-moderation model. Digital wound tracking was associated with significantly shorter healing durations (15 vs. 35 days) and fewer clinic nursing visits (3 vs. 5.8 visits) compared to standard nursing monitoring. Digital tracking demonstrated improved wound size reduction over time. Laboratory values tested did not consistently predict healing outcomes. Digital tracking exhibited moderate negative correlations with the total number of nursing visits. Regression analysis identified wound complexity, hospitalizations, and initial wound size as clinical predictors for more nursing visits in patients with diabetes mellitus (p < .01). Digital tracking significantly reduced the number of associated nursing visits for patients with peripheral vascular disease. These findings suggest that digital wound management may streamline nursing care and provide advantages, particularly for comorbid populations facing treatment burdens. This study adhered to STROBE guidelines in reporting this observational research. By streamlining documentation and potentially shortening healing times, digital wound tracking could help optimise nursing resources, enhance wound care standards, and improve patient experiences. This supports further exploration of digital health innovations to advance evidence-based nursing practice. This study involved retrospective analysis of existing patient records and did not directly include patients or the public in the design, conduct, or reporting of the research.

Utilization of MQTT and Radio GPS in Pet Monitoring

This research presents an effective and efficient solution for tracking pets using Message Queuing Telemetry Transport (MQTT) based on radio GPS. The study creates a device and application capable of monitoring the whereabouts of pets to prevent their loss by displaying the latitude and longitude of the pet's location. This application provides real-time monitoring and exhibits high accuracy, with minimal differences in latitude and longitude compared to both animal tracking applications and Google Maps. The application's update speed is also impressive, with a delay of only 4.25 seconds. By continuously developing and enhancing this technology, it is expected to provide broader benefits to the pet-loving community.

Evolution of CNNs in human tracking applications

Tracking the movement of objects in videos or footage from CCTV systems plays an integral role in crime investigations, surveillance, security predictions, and many other domains. Historically, this task was primarily entrusted to dedicated observers or analysts who would be summoned to review pre-recorded footage post-event. With the advent of machine learning and AI, convolutional neural networks (CNNs) have paved the way for computers to augment human capabilities in analyzing streaming videos or archived recordings. Among the various tracking methodologies that machine learning offers, YOLO (You Only Look Once) and R-CNN (Region-based Convolutional Neural Networks), along with its iterations, stand out as some of the most reliable and precise. However, the scope of analysis often extends beyond these techniques. To enhance accuracy and provide an adept classification mechanism, the Deep SORT (Simple Online and Realtime Tracking) algorithm emerges as pivotal. Its synergy with human detection remains a significant area of discussion and will be deliberated upon in this study. This review aims to elucidate the intricacies of these state-of-the-art methods and their interplay in modern tracking systems.

Does fiducial marker visibility impact task performance and information processing in novice and low-time pilots?

Invisible fiducial markers are introduced for localization of Areas Of Interest (AOIs) in mobile eye tracking applications. Fiducial markers are made invisible through the use of film passing Infra-Red (IR) light while blocking the visible spectrum. An IR light source is used to illuminate the markers which are then detected by an IR-sensitive camera, but which are imperceptible by the human eye. We provide the first empirical study that demonstrates such invisible markers are not distracting to a given task, as demonstrated in a flight simulator where distraction of visible and invisible markers are compared between experienced and novice pilots. Fixation frequency and subjective distraction scores showed that visible markers disrupted natural gaze behavior, particularly in novice pilots. Our findings show that invisible markers should be used when there is a need for them to remain inconspicuous.