User Tracking Research Articles

Retracted: QoS‐Aware Smart Phone‐Based User Tracking Application to Prevent Outbreak of COVID‐19 in Saudi Arabia

Retracted: QoS‐Aware Smart Phone‐Based User Tracking Application to Prevent Outbreak of COVID‐19 in Saudi Arabia

A Personalized Navigation Route Recommendation Strategy Based on Differential Perceptron Tracking User's Driving Preference.

With the increasing frequency of autonomous driving, more and more attention is paid to personalized path planning. However, the path selection preferences of users will change with internal or external factors. Therefore, this paper proposes a personalized path recommendation strategy that can track and study user's path preference. First, we collect the data of the system, establish the relationship with the user preference factor, and get the user's initial preference weight vector by dichotomizing the K-means algorithm. The system then determines whether user preferences change based on a set threshold, and when the user's preference changes, the current preference weight vector can be obtained by redefining the preference factor or calling difference perception. Finally, the road network is quantized separately according to the user preference weight vector, and the optimal path is obtained by using Tabu search algorithm. The simulation results of two scenarios show that the proposed strategy can meet the requirements of autopilot even when user preferences change.

Machine Learning and Cloud Computing Case Study for Online App Usage Tracking

Scalability, data processing, and real-time insights are not present in traditional online app usage tracking. ML can overcome these restrictions and open new possibilities with cloud data, in accordance with this study. In our case study, cloud data and ML boosted how a fitness software tracked user activity hours. Cloud systems store and manage massive amounts of user activity logs, device data, and network traffic of program usage. ML systems can learn trends, predict user behavior, and gain insights from this data. Fitness app data accuracy, user experience, and scalability improved with these technologies. On other web app domains, this method can improve user engagement tracking and personalization.

Privacy-preserving solution for vehicle parking services complying with EU legislation.

Today, many modern cities adopt online smart parking services as best practices. Citizens can easily access these services using their smartphones or the infotainment panels in their cars. These services' primary objective is to give drivers the ability to quickly identify free parking slots, which should reduce parking time, save fuel, and relieve traffic in urban areas. However, the privacy offered by these services should be comparable to that of the standard paper-based parking solutions offered by parking ticket machines. On the other hand, a privacy-preserving smart parking service's design may raise a number of issues, including how to prevent double or multiple uses of parking tickets, how to prevent user tracking and profiling, how to revoke malicious users, how to handle data statistics without violating users' privacy, and how to comply with regulations like the General Data Protection Regulation (GDPR). In this article, we present multidisciplinary research on a comprehensive vehicle parking system that protects users' privacy. The research includes a range of topics, from the examination of regulatory compliance to the design of privacy-preserving parking registration and vehicle parking services to the implementation of privacy-preserving parking data processing features for data analysts. We provide a security analysis of our concept as well as several experimental results.

Using a Smart Living Environment Simulation Tool and Machine Learning to Optimize the Home Sensor Network Configuration for Measuring the Activities of Daily Living of Older People

This paper describes a methodology to optimize the home sensor network to measure the Activities of Daily Living (ADLs) of older people using Machine Learning (ML) applied to synthetic data generated via a newly developed Smart Living Environment (SLE) simulation tool. A home sensor network consisting of Passive InfraRed (PIR) and door sensors allows people to age in place, avoiding invasiveness of the technology by keeping track of the older users’ behaviour and health conditions. However, it is difficult to identify a priori the optimal sensor network configuration to measure users’ behaviour. To ensure better user acceptability without losing measurement accuracy, the authors proposed a methodology to optimize the home sensor network consisting of simulating human activities, and therefore sensor activations, in the reconstructed SLE and analysing the datasets generated through ML. Four ML classifiers, namely the Decision Tree (DT), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), were tested to measure the accuracy of ADL classification. Optimization analysis was made, providing the most suitable home sensor network configuration for two home environment case studies by exploiting the DT classifier results, as it proved to achieve the highest mean accuracy (over 94%) in measuring ADLs.

Surgical instrument recognition for instrument usage documentation and surgical video library indexing

ABSTRACT Temporally locating and classifying instruments in surgical video is useful for the analysis and comparison of surgical techniques. This paper aims to apply action segmentation techniques to temporally segment and classify surgical instruments, and to highlight the utility of this modelling approach through example applications. This paper shows that the action segmentation transformer (ASFormer) architecture with an EfficientNetV2 featurizer performs significantly better in mean average precision than any previous approaches to this task on the Cholec80 dataset. The ASFormer also outperforms Long Short-Term Memory (LSTM) and Multi-Stage Temporal Convolutional Network (MS-TCN) architectures with the same featurizer. This model reduces the need for costly human labelling of surgical video, driving the development of indexed surgical video libraries and instrument usage tracking applications. Examples of these applications are included after the results.

Towards Improving Privacy and Security of Identity Management Systems Using Blockchain Technology: A Systematic Review

An identity management system (IDMS) manages and organizes identities and credentials information exchanged between users, identity providers (IDPs), and service providers (SPs) to ensure confidentiality and enhance privacy of users’ personal data. Traditional or centralized IDMS rely on a third party to store a user’s personal information, authenticate the user, and organize the entire process. This clearly constitutes threats to the privacy of the user, in addition to other issues, such as single point of failure (SPOF), user tracking, and data availability issues. Blockchain technology has many useful features that can contribute to solving traditional IDMS issues, such as decentralization, immutability, and anonymity. Blockchain represents an attractive solution for many issues related to traditional IDMS, including privacy, third-party control, data leakage, and SPOF, supported by Distributed Ledger Technology (DLT) security features and powerful smart contracts technology. The current study presents a systematic literature review and analysis for recently proposed solutions that adopt the traditional centralized approach, as well as solutions based on blockchain technology. The study also aims to provide a deep understanding of proposed IDMS solutions and best practices, and highlight the research gaps and open issues related to IDMSs and users’ privacy. In particular, the current research focuses on analyzing the blockchain-based solutions and illustrating their strengths and weaknesses, as well as highlighting the promising blockchain technology framework that can be utilized to enhance privacy and solve security issues in a centralized IDMS. Such a study is an important step towards developing efficient solutions that address the pressing needs in the field.

Evaluation of a statewide collaborative focused on antibiotic usage surveillance and stewardship in New York State Hospitals

To prepare NYS hospitals for reporting in The National Health Care Safety Network's Antimicrobial Use and Resistance (AUR) Module, the Health care Association of New York State (HANYS) launched a voluntary Antibiotic Stewardship Collaborative (ASC) in late 2015 with 2 aims (1) assist hospitals in developing the infrastructure necessary to track and report antibiotic usage; (2) educate hospitals on antibiotic stewardship. This study evaluates the characteristics of hospitals opting to participate in the ASC and their experiences in the program, as well as the effects of one year of participation (2016) on hospital-acquired C. difficile infection (HA-CDI) rates. Difference in means testing of clinical and non-clinical characteristics were performed to understand the "type" of hospital joining the ASC; semi-structured interviews were conducted to understand reasons for opting in or out of the ASC and experiences in the program; and a multivariate regression analysis with a difference-in-differences approach was used to assess the impact on HA-CDI rates. Hospitals with a greater number of annual discharges (P < .001) located in urban areas (P=.03) were more likely to join the ASC. All participants in the ASC (N=44/184) successfully implemented the necessary infrastructure to track and report antibiotic usage data, despite this being the most cited challenge and main reason hospitals opted not to participate. While HA-CDI rates decreased to a greater extent for participating hospitals (β=-0.153), this was not statistically significant (P=.191). HANYS' ASC proved an effective and well-received strategy for encouraging hospitals, particularly large, urban facilities, to take concrete steps to strengthen their antibiotic stewardship efforts and prepare for potential mandates requiring antibiotic usage tracking and reporting. However, a reduction in HA-CDI resulting from these efforts remains to be seen.

Abstract 15516: A Nationally Representative Evaluation of Patients With Established Cardiovascular Disease and Cardiovascular Risk Factors Achieving Health Goals With Electronic Devices in the United States

Background: Electronic devices, like tablets and smartphones, may help users track and manage cardiovascular (CV) risk factors. However, data on their effectiveness and equitable distribution are scarce. We aimed to investigate the patterns and predictors of achieving health goals using tablets/smartphones among patients with CVD or with CV risk factors. Methods: We used data from the nationally representative Health Information National Trends Survey 5 (HINTS5) from 2017 to 2020. In a validated questionnaire, tracking a health-related goal (quitting smoking, losing weight, or increasing physical activity) using tablets/smartphones was assessed in patients with CVD (ischemic heart disease and congestive heart failure) or CV risk factors (smoking, obesity, hypertension, and diabetes), for magnitude and demographic predictors of success. Results: Among 16,092 participants of HINTS5, 1,606 had CVD (68±14 years, 47% women), and 8,984 were at risk of CVD (59±15 years, 59% women). During 2017-2020, compared with an estimated 46% (95% CI: 44-47%) of the US adult population, 33% (95% CI: 28-38%) of patients with CVD and 43% (95%CI: 41-45%) of patients at-risk of CVD successfully reported tracking their progress to achieve a CV health goal using tablets/smartphones. Younger age, female sex, non-Hispanic Black race, and higher income were significantly associated with higher proportion reporting improving their CV health, among patients with and at-risk of CVD. Discussion: Nearly half of patients with CV risk factors successfully manage health habits using electronic devices, but given large underuse among demographic groups with frequent adverse cardiovascular outcomes, a systematic approach to their use in clinical care may help improve patient outcomes.

Abstract 13358: A Platform for the Personalized Management of Diabetes and Cardiovascular Disease at Population Scale With Data From Multiple Sensors

Introduction: The American Heart Association has identified the design of mobile health (mHealth) tools to improve the management of diabetes and cardiovascular disease and to prevent unplanned hospital readmissions as a priority. Methods: We developed HEART, an open source platform for personalized telehealth at population scale based on data from internet-connected health sensors and data extracted from the electronic medical record (EMR). Health sensors include scales, blood pressure monitors, activity monitors, continuous glucose monitors, and insulin pumps. EMR data include gender, age, race, reason for admission, all surgical procedures performed, and all medications prescribed upon discharge. We collected admission and readmission data for all cardiology patients at an academic pediatric medical center over the last 7 years. Results: HEART has five independent modules (Figure): 1) a data processing module that pulls data in from a variety of devices and EHR feeds, 2) an algorithm module that identifies reasons why a provider may want to contact a patient and ranks patients for contact, 3) a visual interface module that summarizes population data and provides additional details for patients selected by the provider, 4) an intervention module that facilitates messages being sent to providers and documented in the EMR, and 5) a user tracking module that monitors providers use. HEART ranks the entire population cared for by a clinic and allows providers to identify easily those patients who may benefit most from a secure message, telemedicine contact, or in-person visit. There were 5,011 admissions to cardiology among 2,916 patients, with 1000 urgent readmissions for 512 patients and 35,300 outpatient clinic visits. Conclusions: HEART is a population-level framework to facilitate personalized mHealth with data from a variety of sensors and EMR data feeds. It may facilitate outpatient monitoring as part of an effort to prevent unplanned hospital readmission.

Surface structure on abandoned upland blanket peatland tracks

Temporary permissions are often granted for track use on peatlands. However, even when peatland track designs attempt to minimise environmental impacts via use of mesh systems, such linear disturbances may have persistent impacts. We evaluated the surface peatland structure of five abandoned tracks (four with a mesh surface, one unsurfaced) with varying past usage frequencies, at an upland site in northern England. Simplification of the surface nanotopography was found on all tracks compared to surrounding control areas, with increased micro-erosion patterns in rutted areas, and invasive species on some treatments. The frequency of previous usage was not found to be a significant factor controlling nano-topographic loss. Edge effects and hillslope position were influential in places, but these effects were not consistent across treatments. Nano-topographic recovery was found to be inhibited when track usage commenced within a short time frame after track construction. Mesh tracks appear to create a spatial constraint leading to poor development of plants and a reduced ability to form characteristic structures which are integral to mire function.

Device-based Real-time Single User Indoor Localization Using Internet of Things

Background: Localization is an important area of implementation of the internet of things based on Wireless Sensor Networks. Outdoor user tracking is possible using the global positioning system; however, the global positioning system accuracy decreases in indoor environments. To overcome this problem, the wireless sensor network is used in internet of things-based technology for localization. Objective: The wireless sensor network-based indoor localization is categorized into two categories; rangebased and range-free localization. In range-based localization, it first computes the relative distance and then calculates relative coordinates. In range-based techniques, distance and position are calculated. The internet of things-based localization uses the range-based and range-free techniques of a wireless sensor network to localize any object. The Light Dependent Resistor-based localization work has been proposed in previous research. In this research, the light-dependent resistor traces a person’s entry /exit event as the person switches ON/OFF lights of the building. However, it was not a sufficient effort to localize a person using light dependent resistor. To overcome the problem of light dependent resistor, the two PIR sensors have been used in each room along with one RFID based approach in this study. Methods: An indoor scenario has been considered in this study. The hardware setup has been configured to trace the user. When a user enters inside a building, he will switch on the lights, and the light sensor records the light intensity and gives some reading. The difference in the reading of the light sensor (before switching ON the light and after switching OFF the light) gives some clue about a user in an indoor scenario. Nevertheless, if the lights of many rooms remain switched ON, then the user cannot be localized using the above method. In order to sort out this ambiguity of light sensors, two passive infrared sensors in each room along with one radio frequency identification-based model have been proposed in the present study. Implementing the single-user localization using a light-dependent resistor sensor becomes erroneous if a person moves from one room to another and the lights get turned ON/OFF. Moreover, the LDR-based model is affected by sunlight during the daytime. Results: As the implementation of passive infrared sensors along with one RFID-based localization technique gives 93% efficiency, the light-dependent resistor-based localization system gives an efficiency of 35%. Conclusion: The light-dependent resistor-based approach is prone to more errors because the user may enter multiple rooms while the lights of each room remain ON. To overcome this problem, a passive infrared sensor and radio frequency identification-based approach for a single user indoor localization has been proposed. The proposed techniques are easy and cost-effective for implementation. The results show that the proposed technique provides better localization accuracy than a light-dependent sensor-based technique for single-user indoor localization.

Estimating indoor crowd density and movement behavior using WiFi sensing

The fact that almost every person owns a smartphone device that can be precisely located is both empowering and worrying. If methods for accurate tracking of devices (and their owners) via WiFi probing are developed in a responsible way, they could be applied in many different fields, from data security to urban planning. Numerous approaches to data collection and analysis have been covered, some of which use active sensing equipment, while others rely on passive probing, which takes advantage of nearly universal smartphone usage and WiFi network coverage. In this study, we introduce a system that uses WiFi probing technologies aimed at tracking user locations and understanding individual behavior. We built our own devices to passively capture WiFi request probe packets from smartphones, without the phones being connected to the network. The devices were tested at the headquarters of the research sector of the Elm Company. The results of the analyses carried out to estimate the crowd density in offices and the flows of the crowd from one place to another are promising and illustrate the importance of such solutions in indoor and closed spaces.

Development of Aerial Interface by Integrating Omnidirectional Aerial Display, Motion Tracking, and Virtual Reality Space Construction

By combining an aerial display, user tracking system, and virtual reality (VR) space, we realized an aerial interface for providing both an immersive user experience and real-time communication with people outside the device. We developed a simple application for detecting user contact and changing the color of an object to measure the latency of our aerial interface system.

Photonic Delay Processing With Centralized Etalon Cascade for Radio Beamsteering in Simplified 2 × 5 Phased-Array Radio Heads

The adoption of beam-centric radio communication schemes calls for efficient methods of beamforming with multi-element antennas. We present a delay generation method that employs a shared and centralized cascade of Gires-Tournois etalons for element-by-element radio frequency beamsteering at remote radio heads, thus adding no complexity at the antenna site. We experimentally demonstrate beamsteering of up to 32° in combination with a 2 × 5 phased-array antenna operated at a radio carrier frequency of 3.5 GHz and transmitting 64-ary quadrature amplitude modulated, orthogonal frequency division modulated radio signals. The radio transmission performance over mobile optical fronthaul and free-space radio propagation is evaluated through offline error vector magnitude estimation and real-time transmission of high-definition video traffic. We further show carrier phase switching at 2 kHz using direct emission frequency modulation of the involved optical light sources, proving the proposed concept to support the fast tracking of mobile users.

Investigating relationship between using smartphones for learning and academic performance of secondary school students in Nigeria

Students are the primary users and recipients of smartphones and their persuasiveness has an impact on the general public. Several studies have found that there is a strong relationship between the use of smartphones and educational performance, as it led to behaviors that impair students' careers and learning habits. However, these studies only looked at smartphone usage patterns and presented a limited number of them. It is being proposed that the gap can be bridged by integrating smartphone usage and academic performance to keep track of users. The study involved 90 students in selected secondary schools in Sokoto State. According to the study, elements such as design functionality, design appeal, and design navigation influence students’ use of smartphones. Students are more likely to utilize their smartphones if they can quickly download pages, access e-learning systems and navigate between pages. It was also discovered that secondary school students in Sokoto frequently used their smartphones for some school learning activities, enhancing the perceived value of utilizing a smartphone for learning. They use their smartphones to access and share learning resources, chat with classmates and teachers, look up solutions to tasks, and even learn new things at any time from anywhere. In general, the study shows that using smartphones for learning improves general knowledge and has a good impact on secondary school students' academic performance.

A Social Media-Based Intervention for Chinese American Caregivers of Persons With Dementia: Protocol Development.

BackgroundRacial/ethnic minority and immigrant caregivers of persons with dementia experience high rates of psychosocial stress and adverse health outcomes. Few culturally tailored mobile health (mHealth) programs were designed for these vulnerable populations.ObjectiveThis study reports the development of a culturally tailored mHealth program called Wellness Enhancement for Caregivers (WECARE) to improve caregiving skills, reduce distress, and improve the psychosocial well-being of Chinese American family caregivers of persons with dementia.MethodsCommunity-based user-centered design principles were applied in the program development. First, the structure and curriculum of the WECARE program were crafted based on existing evidence-based interventions for caregivers with input from 4 experts. Second, through working closely with 8 stakeholders, we culturally adapted evidence-based programs into multimedia program components. Lastly, 5 target users tested the initial WECARE program; their experience and feedback were used to further refine the program.ResultsThe resulting WECARE is a 7-week mHealth program delivered via WeChat, a social media app highly popular in Chinese Americans. By subscribing to the official WECARE account, users can receive 6 interactive multimedia articles pushed to their WeChat accounts each week for 7 weeks. The 7 major themes include (1) facts of dementia and caregiving; (2) the enhancement of caregiving skills; (3) effective communication with health care providers, care partners, and family members; (4) problem-solving skills for caregiving stress management; (5) stress reduction and depression prevention; (6) the practice of self-care and health behaviors; and (7) social support and available resources. Users also have the option of joining group chats for peer support. The WECARE program also includes a back-end database that manages intervention delivery and tracks user engagement.ConclusionsThe WECARE program represents one of the first culturally tailored social media–based interventions for Chinese American caregivers of persons with dementia. It demonstrates the use of community-based user-centered design principles in developing an mHealth intervention program in underserved communities. We call for more cultural adaptation and development of mHealth interventions for immigrant and racial/ethnic minority caregivers of persons with dementia.

AI-based indoor localization using mmWave MIMO channel at 60 GHz

In recent years, indoor localization using wireless systems has been an important area of research for its applications towards health, security and the tracking of users. A Global Positioning System (GPS) is considered as the best solution for localization for outdoor scenarios but it fails to provide accurate positioning for indoor scenarios. Wi-Fi fingerprinting methods using received signal strength from multiple access points are popular for solving indoor localization problem. As the wireless systems move towards higher frequencies, higher bandwidth and a large antenna array, sensing has also become feasible along with communication, which is an important research area towards 6G named as Integrated Communication And Sensing (ISAC). ISAC relies on sensing parameter estimations, such as estimation of fine range, Doppler and angular information which contains the signature of the surrounding objects. A localization problem can be solved by analysing the sensing parameters. In this paper, we propose a solution for the localization problem for IEEE 802.11ay WLAN systems based on signal processing and Machine Learning (ML) in indoor scenarios. (...)

An Adaptive User Tracking Algorithm Using Irregular Data Frames for Passive Fingerprint Positioning.

Wi-Fi fingerprinting is the most popular indoor positioning method today, representing received signal strength (RSS) values as vector-type fingerprints. Passive fingerprinting, unlike the active fingerprinting method, has the advantage of being able to track location without user participation by utilizing the signals that are naturally emitted from the user’s smartphone. However, since signals are generated depending on the user’s network usage patterns, there is a problem in that data are irregularly collected according to the patterns. Therefore, this paper proposes an adaptive algorithm that shows stable tracking performances for fingerprints generated at irregular time intervals. The accuracy and stability of the proposed tracking method were verified by experiments conducted in three scenarios. Through the proposed method, it is expected that the stability of indoor positioning and the quality of location-based services will improve.

The Influence of Transparency and Control on the Willingness of Data Sharing in Adaptive Mobile Apps

Today, adaptive mobile applications use mobile sensing and user tracking, allowing for adaptation to the users' context and needs. This raises several privacy concerns. Privacy dashboards provide transparency and sharing control; however, their impact on the users' behavior is unclear. To shed light on the effects of (a) transparency and (b) control features, we developed a mobile sensing privacy dashboard and evaluated it in the wild (N=227). We found that the pure presentation of raw logging data is rather deterring, and users tend to use the app less, but offering the user control over the data collection can compensate for that. Users used the control features rarely and, as such, did not affect the data collocation. Our work informs the design of future privacy-enhancing interfaces in applications relying on passively collected mobile sensing data. Moreover, our results encourage the adoption of privacy dashboards in the applications and relieve developers from concerns about the negative influences of transparency information on the quality of collected data.