Person Tracking Research Articles

Data donation of personal physical activity trackers

Physical inactivity is a growing worldwide concern. Population monitoring of physical activity (PA) is generally done using questionnaires, yet recently there has been a strong interest in more objective forms of measurement using wearable activity trackers. Depending on the prevalence of personal activity trackers, respondents could be invited to share the data from their own devices. Adherence to PA guidelines could then be determined with the help of the donated data. In this study, we explored two different methods of data donation to measure PA: uploading of spreadsheets and manual copying of data into questionnaires. Next to the response and representativity of those willing to donate, we compared the adherence to the PA guidelines determined using the different methods. The results showed that the prevalence of personal activity trackers is still limited and biased with age, education and adherence to the PA guidelines. The majority of tracker owners were willing to copy their data in a questionnaire, whereas uploading a spreadsheet proved to be difficult. The sample actually able to donate the required data is small and selective and therefore population monitoring of PA using donated data of personal trackers should currently be regarded as unfeasible.

Providing Context to the "Unknown": Patient and Provider Reflections on Connecting Personal Tracking, Patient-Reported Insights, and EHR Data within a Post-COVID Clinic

COVID Long Haul (CLH) is an emerging chronic illness for which the healthcare system continues to seek a common understanding of symptoms, diagnosis, and treatment. CLH experiences can differ drastically, necessitating personalized care plans. Because patients interact with different clinicians during their CLH journey, it becomes important to ensure interoperability and understand clinical relevance of different data that can support clinicians in making appropriate recommendations. We conducted qualitative research where we interviewed 13 patients, conducted a focus group with 8 clinicians, and analyzed care plan follow-up records. We report patient and clinician expectations from and interactions with clinic data. We uncover logistical challenges, personal contexts, and health barriers impacting patient compliance. As researchers embedded in the clinical system, we identify the potential of using multiple patient data streams to support personalized treatment and clinical decisions. We discuss technology design opportunities and provide actionable recommendations for improving clinical workflows and cross-provider collaboration.

Implementation of innovative technologies in the training process of martial artists

Purpose: to establish leading directions for the implementation of innovative technologies in the practical activities of coaches and athletes in martial arts. Material and methods. A systematic review of scientific publications for the period from 2020 to 2024 related to the application of innovations in the sports field was carried out. The search was conducted in Google Scholar, PubMed, Scopus and Web of Science databases using the following keywords: «innovative technologies», «digital technologies», «sports», «training», «martial arts». The search was conducted by article titles. Full-text articles of a practical or review nature were analyzed. Results: it was established that the use of innovative technologies in the training of martial artists is an important component of the modernization of the training process. It contributes to improving the organization and control of training, increasing their effectiveness, forming training programs using remote technologies. This helps to create flexible training programs and predict their effectiveness. As a result of the bibliographic analysis of relevant studies, it was established that the key directions of the introduction of innovative technologies in the process of training martial artists are: digitization of individual components of training activities of athletes; introduction of technologies based on artificial intelligence; use of virtual and augmented reality technologies; systematic use of mobile applications and personal trackers in the training of athletes; the use of distance learning technologies in the organization of training activities. Along with the established advantages of using innovations in martial arts training, the problems of their implementation in the process of training athletes were identified, which consists in: limited availability of modern technologies to athletes and martial arts coaches; the need to develop programs for the use of technologies by participants in the training process. Conclusions. The use of innovative technologies is an important factor in the modernization of the training process of martial arts athletes. They are aimed at optimizing the training of martial arts athletes, reducing the risk of injuries and increasing the level of individualization of training. Despite the identified potential, the number of studies devoted to the practical implementation of innovations in sports training of martial artists is insufficient. This is due to the complexity of the wide application of technologies and the rapid pace of their modernization, which makes it difficult to assess their long-term effectiveness in various types of martial arts. In general, the findings of this study confirm that innovative technologies are a key driver of qualitative changes in the process of training martial arts athletes. Further research will focus on the development of comprehensive programs using digital technologies at the initial stage of multi-year martial arts training.

Development of a Passenger OD Estimation System for Route Buses Using Person Tracking Techniques and Matching Algorithms

Development of a Passenger OD Estimation System for Route Buses Using Person Tracking Techniques and Matching Algorithms

Numerical Modeling, Analysis, and Optimization of RFID Tags Functioning at Low Frequencies

RFID (radio frequency identification) tags play a crucial role in a wide range of applications, from wireless communications to personal tracking and smart city infrastructure. These tags come in various shapes and sizes, prompting the authors to review the specialized literature and focus on optimizing planar designs with different geometries. This study prioritizes reducing the size of the most commonly used tags while enhancing their reliability. The primary objective of this article is to understand and improve the performance of planar RFID tags operating at 13.56 MHz through numerical simulations based on structures generated by algorithms developed in MATLAB. Building on previous research, the methodology is validated, followed by a detailed description of the algorithm designed and implemented by the authors in MATLAB to identify all possible structures that meet the design criteria. The authors compared various analyzed structures, considering different inductor shapes, dielectric materials, and thicknesses while examining their effects on gain and resonant frequency. The study also provides thermal analysis of the structures, and experimental validation of the studied designs. Finally, the researchers conclude with recommendations on the optimal structure for RFID tags.

A FORENSICS ACTIVITY LOGGER TO EXTRACT USER ACTIVITY FROM MOBILE DEVICES

Mobile devices have become one of the most often used tools in everyday life, mostly because of the importance of its apps. In this case, mobile devices become personal trackers for daily activities that provide important information about the user by recording extra data in addition to the user's personal information. As a consequence of this information gathering, several tools are now accessible for use on mobile devices, however each tool is only able to provide discrete details about a certain application or activity. Consequently, the present research proposes a technology that allows investigators to get a detailed report and time line of all operations performed on the device. This report combines data from several sources to generate a unique collection of facts. Furthermore, an example is provided to illustrate how the solution works, highlighting the practicality of the instrument as well as the way in which investigators need to use it.

Real-Time Indoor Visible Light Positioning (VLP) Using Long Short Term Memory Neural Network (LSTM-NN) with Principal Component Analysis (PCA).

New applications such as augmented reality/virtual reality (AR/VR), Internet-of-Things (IOT), autonomous mobile robot (AMR) services, etc., require high reliability and high accuracy real-time positioning and tracking of persons and devices in indoor areas. Among the different visible-light-positioning (VLP) schemes, such as proximity, time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), and received-signal-strength (RSS), the RSS scheme is relatively easy to implement. Among these VLP methods, the RSS method is simple and efficient. As the received optical power has an inverse relationship with the distance between the LED transmitter (Tx) and the photodiode (PD) receiver (Rx), position information can be estimated by studying the received optical power from different Txs. In this work, we propose and experimentally demonstrate a real-time VLP system utilizing long short-term memory neural network (LSTM-NN) with principal component analysis (PCA) to mitigate high positioning error, particularly at the positioning unit cell boundaries. Experimental results show that in a positioning unit cell of 100 × 100 × 250 cm3, the average positioning error is 5.912 cm when using LSTM-NN only. By utilizing the PCA, we can observe that the positioning accuracy can be significantly enhanced to 1.806 cm, particularly at the unit cell boundaries and cell corners, showing a positioning error reduction of 69.45%. In the cumulative distribution function (CDF) measurements, when using only the LSTM-NN model, the positioning error of 95% of the experimental data is >15 cm; while using the LSTM-NN with PCA model, the error is reduced to <5 cm. In addition, we also experimentally demonstrate that the proposed real-time VLP system can also be used to predict the direction and the trajectory of the moving Rx.

Object and person tracking systems to enable extremity dosimetry in nuclear medicine using computational methods

Nuclear medicine (NM) professionals are potentially exposed to high doses of ionising radiation, particularly in the skin of the hands. Ring dosimeters are used by the workers to ensure extremity doses are kept below the legal limits. However, ring dosimeters are often susceptible to large uncertainties, so it is difficult to ensure a correct measurement using the traditional occupational monitoring methods. An alternative solution is to calculate the absorbed dose by using Monte Carlo simulations. This method could reduce the uncertainty in dose calculation if the exact positions of the worker and the radiation source are represented in these simulations. In this study we present a set of computer vision and artificial intelligence algorithms that allow us to track the exact position of unshielded syringes and the hands of NM workers. We showcase a possible hardware configuration to acquire the necessary input data for the algorithms. And finally, we assess the tracking confidence of our software. The tracking accuracy achieved for the syringe detection was 57% and for the hand detection 98%.

IOT based Autonomous Location Surveillance System Using PNT Services

The entire set of interconnected devices as well as the technology enabling communication between the devices and the cloud. This article presents an innovative Internet of Things (IOT)based autonomous location monitoring system that is made possible by Positioning, Navigation, and Timing (PNT) services. The system allows for real-time tracking and surveillance of objects or persons by combining Internet of Things sensors and communication modules with PNT technologies like GPS, Galileo, or GLONASS. The technology uses advanced machine learning techniques and algorithms to automatically identify and evaluate anomalies in the monitored area. It then swiftly reports these findings to the relevant authorities.PNT services are seamlessly linked to ensure accurate and reliable location, even in challenging Environment. In addition to being adaptable, scalable, and compatible with existing infrastructure, the Technology has a wide range of applications, including border security, asset protection, and perimeter monitoring. This work advances the possibilities of autonomous surveillance systems. In depth features provided by the research include mobile number identification (subject to legal and regulatory constraints), real-time location visualization, and centralized vehicle data management for enhanced fleet management, security, and operational effectiveness The development of sophisticated solutions that make use of precision navigation and timing (PNT) and the internet of things (IOT) has been spurred by the growing need for effective location based surveillance systems. Edge computing units, a central control unit and a monitoring and control user interface are important parts of the system. Data is processed locally with edge computing, which lowers latency and improves response times. to create a self-contained location monitoring system using precise PNT services and IOT technology. Keywords: IOT, Real-Time Tracking, Vehicle Management, Python Flask, Mobile Number Tracking, Data Fetching

Deep video-based person re-identification (Deep Vid-ReID): comprehensive survey

Person re-identification (ReID) aims to find the person of interest across multiple non-overlapping cameras. It is considered an essential step for person tracking applications which is vital for surveillance. Person ReID could be investigated either using image-based or video-based. Video-based person ReID is considered more discriminating and realistic than image-based ReID due to the massive information extracted for each person. Different deep-learning techniques have been used for video-based ReID. In this survey, recently published articles are reviewed according to video-based ReID system pipeline: deep features learning, deep metric learning, and deep learning approaches. The deep feature learning approaches are categorized into spatial and temporal approaches, while deep metric learning is divided into metric and metric learning approaches. The deep learning approaches are differentiated into: supervised, unsupervised, weakly-supervised, and one-shot learning. A detailed analysis is held for the architectures of the state-of-the-art deep learning approaches. And their performance on four benchmark datasets is compared.

Using personal tracking software data to aid the impact of illegal trails in national parks

ABSTRACT The use of personal tracking devices for recreational use and for safety when in the outdoors has grown exponentially in recent years. The large-scale data collected through these platforms has enabled our team to gain a greater understanding of the way in which people encroach onto National Parks causing environmental damage. The project used freely accessible data on the access of illegal trails primarily from motor bike and mountain bike use in the East Otway Range of Victoria. This region was an excellent case study as it is a mosaic of state and national parks and the signage and restrictive fences place by national parks have been aligned with the illegal trail formation. The data mapping has been coupled with measurement of the impact on the environment including the number and scale of the trails and the type of environment they are impacting including fauna and flora of national significance. The data has shown some useful information around the manner in which illegal trails are identified and concealed by users. Further, the research has uncovered ways in which trails have been concealed from authorities.

Flexible minimalist self-tracking to support individual reflection

Self-tracking technology can help users develop new knowledge about themselves, supporting their health and general wellbeing. Most of these devices inform users about their lives by autonomously generating data about highly constrained topics. Recent research has started to explore more flexible, manual self-tracking systems, which allow users to express themselves through their data and learn by engaging with their experiences in-the-moment. This paper describes a field trial using the Chromatize app, designed to explore the value of minimalism and flexibility – including in data histories – in personal tracking systems. Our data indicates that low-burden experience logging systems which focus on the creation of symbolically-rich, self-defined data can support self-awareness, reflection and regulation at opportune moments. The analysis also contributes a formulation of reflection in the context of self-tracking technologies that is more meaningful than the loose use of ‘reflection’ used in much of the literature.

Developing a SmartRail Security System with YOLO and OpenCV

SmartRail Security System is an innovative research designed to significantly enhance security and safety measures within the station. The primary objective of this research is to implement advanced surveillance technology, revolutionizing the security infrastructure of the railway station and ultimately creating a smarter and more efficient approach to protecting the station environment completely. The SmartRail security system prioritizes key features aimed at enhancing rail station security System. It incorporates state-of-the-art missing person tracking capabilities that allow rapid identification and monitoring of individuals with previous missing records within the station premises. The system's goal is to provide real-time tracking and analysis, ensuring proactive measures are taken to prevent and respond swiftly to security threats. The purpose of the SmartRail security system is to combat illegal intrusion inside the restricted area of the railway station. The aim of this system is to create a strong monitoring system by introducing smart monitoring tools. This will strengthen the defenses against unauthorized access and bolster the overall security situation of the station comprehensively. SmartRail security systems go beyond mere surveillance; they promise to redefine safety standards within the station. This smart and efficient approach is deemed essential to ensure the security of staff and the entire station environment.

Region-based convolutional neural networks for occluded person re-identification

In a variety of applications, including intelligent surveillance systems, targeted tracking, and assistive human-following robots, the ability to accurately identify individuals even when they are partially obscured is imperative. Such Continuous person tracking is complicated by the close similarity between the appearance of people and target occlusions. This study addresses this significant challenge by proposing a two-step, detection-first approach that uses a region-based convolutional neural network (R-CNN) as the re-identification (re-ID)solution. The model is specifically trained to detect occluded persons at different levels of occlusion before forwarding the image for the re-ID process. Three occluded-specific datasets are selected to evaluate the model's effectiveness in detecting occluded people. There are 379 distinct people in total, and each has five images obstructed from different angles. A sample of the data is taken to simulate various environment settings, and new data points are generated with different degrees of occlusion to assess how well the model performs under varying levels of obstruction. The findings demonstrate that the proposed person re-ID model is reliable in most circumstances, correctly re-identifying at 74% (Rank-1) and 90% (Rank-5). Although there is a decrease in accuracy as the number of distinctive people in the dataset increases, this does not significantly impact the tracking performance in various applications, which are expected to recognize a single person or a small group of individuals. Future works will explore refining similarity matching algorithms by delving into robust image comparison techniques, thereby addressing the challenges presented by occlusions. A critical aspect is to assess the model under diverse lighting conditions and investigate scenarios with multiple individuals in a frame. It is also beneficial to exploit high-resolution datasets, such as DukeMTMC-reID, and integrate finer contextual details, like clothing or carried objects. These collective efforts are essential for optimizing the model’s efficacy in practical applications and advancing person re-ID technologies.

Deep Learning Based Multi-Camera Person Tracking and Re-identification System

This research paper presents a novel Deep Learning Based Multi-Camera Person Tracking and Re-identification System designed to enhance security and surveillance in public spaces. Leveraging advanced technologies including Object Detection through YOLO and Deep SORT, the system offers an efficient solution for monitoring and tracing individuals across multiple camera views. Through object detection, individuals are identified within each camera frame, while Deep SORT ensures seamless tracking as they move across different camera perspectives. Additionally, a person re-identification module enhances tracking accuracy by extracting distinctive features and linking individuals across various camera views using unique identifiers. This system represents a significant advancement in surveillance capabilities, contributing to the development of effective and user-friendly surveillance solutions.

Determinants for university students’ location data sharing with public institutions during COVID-19: The Italian case

Abstract Data on real-time individuals’ location may provide significant opportunities for managing emergency situations. For example, in the case of outbreaks, besides informing on the proximity of people, hence supporting contact tracing activities, location data can be used to understand spatial heterogeneity in virus transmission. However, individuals’ low consent to share their data, proved by the low penetration rate of contact tracing apps in several countries during the coronavirus disease-2019 (COVID-19) pandemic, re-opened the scientific and practitioners’ discussion on factors and conditions triggering citizens to share their positioning data. Following the Antecedents → Privacy Concerns → Outcomes (APCO) model, and based on Privacy Calculus and Reasoned Action Theories, the study investigates factors that cause university students to share their location data with public institutions during outbreaks. To this end, an explanatory survey was conducted in Italy during the second wave of COVID-19, collecting 245 questionnaire responses. Structural equations modeling was used to contemporary investigate the role of trust, perceived benefit, and perceived risk as determinants of the intention to share location data during outbreaks. Results show that respondents’ trust in public institutions, the perceived benefits, and the perceived risk are significant predictor of the intention to disclose personal tracking data with public institutions. Results indicate that the latter two factors impact university students’ willingness to share data more than trust, prompting public institutions to rethink how they launch and manage the adoption process for these technological applications.

AN EPIDEMIC MODEL WITH SATURATED DIGITAL CONTACT TRACING FUNCTION: BIFURCATION AND ANALYSIS

In this paper, we develop an epidemic model with a nonlinear function that describes the saturated digital contact tracing. The model is theoretically and numerically analyzed based on its dynamics. The model equilibria points and the basic reproduction number are obtained. However, the proposed model reveals both backward and forward bifurcation, and backward bifurcation occurs when the digital contact tracing saturation parameter is larger than a specific threshold. Real data are used to fit the model and estimate the parameter values. Sensitivity analysis reveals that interventions such as reducing the infection rate, enhancing quarantine efforts, and accelerating vaccination can effectively reduce the basic reproduction number, [Formula: see text]. Simulations show that improving tracing accuracy and encouraging greater participation in providing personal track information can effectively reduce the peak of undetected exposed individuals. Moreover, we analyze the expression for the specific threshold and determine that the increasing hospital resources and strengthening patient quarantine can reduce the potential occurrence of backward bifurcation. The analysis and simulations presented in this paper provide valuable suggestions for the prevention and control of the epidemic.

Becoming Healthier

To meet the challenges of an ageing population, increasing chronic diseases, and rising healthcare costs, Singapore is embarking on a long-term, multi-year strategic reform to shift care to the community, to improve value for resources expended, and to make the prevention of diseases and complications the focus of the health and social care system. Healthier SG will launch in the middle of 2023 for those aged 60 and above.&#x0D; The reform will enlist, integrate and enhance the mostly-private primary care sector into primary care networks. Residents will enrol to a selected primary care provider for long-term care relationships based on individualised health plans incorporating primary and secondary preventive activities like lifestyle improvements, health screenings and vaccinations. Social prescribing to community partners will enhance the prevention of disease and complications. Corollary enablers will also be enhanced, from health and social information sharing, to manpower development, to financial incentives and support. Digital Health capabilities like the national patient information repositories and personal trackers will be part of the intended new health ecology.&#x0D; This presentation will describe the health and social care reform, its origins and drivers and currently intended plan and desired outcomes, and discuss its theory of change, implementational challenges and potential enablers and barriers for success. In particular, we will discuss the difficulties in the systematic measurement, monitoring and evaluation of a large-scale multi-year reform, and the governance and leadership challenges to maintaining coherence and shaping trajectories in such complex endeavours.

People Tracking in Video Surveillance Systems Based on Artificial Intelligence

As security is one of the basic human needs, we need security systems that can prevent crimes from happening. In general, surveillance videos are used to observe the environment and human behavior in a given location. However, surveillance videos can only be used to record images or videos, without additional information. Therefore, more advanced cameras to obtain other additional information such as the position and movement of people. This research extracted this information from surveillance video footage using a person tracking, detection and identification algorithm. The framework for these is based on deep learning algorithms, a popular branch of artificial intelligence. In the field of video surveillance, a person tracking is considered a challenging task. Many computer vision, machine learning and deep learning techniques have been developed in recent years. The majority of these techniques are based on frontal view images or video sequences. In this work, we will compare some previous work related to the same topic.

Designing Data Visualisations for Self-Compassion in Personal Informatics

Wearable personal trackers offer exciting opportunities to contribute to one's well-being, but they also can foster negative experiences. It remains a challenge to understand how we can design personal informatics experiences that help users frame their data in a positive manner and foster self-compassion. To explore this, we conducted a study where we compared different visualisations for user-generated screen time data. We examined positive, neutral and negative framings of the data and whether or not a point of reference was provided in a visualisation. The results show that framing techniques have a significant effect on reflection, rumination and self-compassion. We contribute insights into what design features of data representations can support positive experiences in personal informatics.