Individuals In Real Time Research Articles

Real-Time Surveillance with AI: A Comprehensive Approach to Security and Monitoring

The last ten years have been quite phenomenal for artificial intelligence and computer vision. For the past few years, it was unstoppable, with outstanding breakthroughs that moved towards advanced real-time surveillance systems-that wholesome approach to real-time surveillance through the power of AI in technological advancement for accuracy, scalability, and responsiveness. The system integrates facial recognition, object detection, and anomaly detection algorithms to give a very stable, adaptive response to the various facets of surveillance needs, such as public safety, institutional monitoring, and private security. Through deep learning models extracted from comprehensive datasets, the system is able to identify known individuals in real time and trace the movements and unusual behaviors of these people. A discussion of challenges in data privacy, computational efficiency, and false positives goes hand in hand with an in-depth analysis of system architecture, which includes thorough hardware and software configuration descriptions. The experimental results show the potential of the system in real-world environments as an alternate scalable solution for future security infrastructures.

Granular biopolitics: Facial recognition, pandemics and the securitization of circulation

The COVID-19 pandemic has provided opportunities for facial recognition technology and other forms of biometric monitoring to expand into new markets. One anticipated result is the wholesale reconfiguration of shared and public space enabled by the automated identification and tracking of individuals in real time. Drawing on data from several industry trade shows, this article considers the forms of ‘environmental’ governance envisioned by those developing and deploying the technology for the purposes of security, risk management, and profit. We argue that the ‘contactless culture’ that emerged during the COVID-19 pandemic anticipates the normalization of a form of mass-customized biopolitics: the ability to operate on the population and the individual simultaneously through automated forms of passive identification. This form of governance relies not just on machinic recognition, but on the real-time reconfiguration of physical space through automated access controls and the channelling of both people and information.

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.

Facial Emotion Recognition for Photo and Video Surveillance Based on Machine Learning and Visual Analytics

Modern video surveillance systems mainly rely on human operators to monitor and interpret the behavior of individuals in real time, which may lead to severe delays in responding to an emergency. Therefore, there is a need for continued research into the designing of interpretable and more transparent emotion recognition models that can effectively detect emotions in safety video surveillance systems. This study proposes a novel technique incorporating a straightforward model for detecting sudden changes in a person’s emotional state using low-resolution photos and video frames from surveillance cameras. The proposed technique includes a method of the geometric interpretation of facial areas to extract features of facial expression, the method of hyperplane classification for identifying emotional states in the feature vector space, and the principles of visual analytics and “human in the loop” to obtain transparent and interpretable classifiers. The experimental testing using the developed software prototype validates the scientific claims of the proposed technique. Its implementation improves the reliability of abnormal behavior detection via facial expressions by 0.91–2.20%, depending on different emotions and environmental conditions. Moreover, it decreases the error probability in identifying sudden emotional shifts by 0.23–2.21% compared to existing counterparts. Future research will aim to improve the approach quantitatively and address the limitations discussed in this paper.

IoT-based wearable health monitoring device and its validation for potential critical and emergency applications.

The COVID-19 pandemic brought the world to a standstill, posing unprecedented challenges for healthcare systems worldwide. The overwhelming number of patients infected with the virus placed an enormous burden on healthcare providers, who struggled to cope with the sheer volume of cases. Furthermore, the lack of effective treatments or vaccines means that quarantining has become a necessary measure to slow the spread of the virus. However, quarantining places a significant burden on healthcare providers, who often lack the resources to monitor patients with mild symptoms or asymptomatic patients. In this study, we propose an Internet of Things (IoT)-based wearable health monitoring system that can remotely monitor the exact locations and physiological parameters of quarantined individuals in real time. The system utilizes a combination of highly miniaturized optoelectronic and electronic technologies, an anti-epidemic watch, a mini-computer, and a monitor terminal to provide real-time updates on physiological parameters. Body temperature, peripheral oxygen saturation (SpO2), and heart rate are recorded as the most important measurements for critical care. If these three physiological parameters are aberrant, then it could represent a life-endangering situation and/or a short period over which irreversible damage may occur. Therefore, these parameters are automatically uploaded to a cloud database for remote monitoring by healthcare providers. The monitor terminal can display real-time health data for multiple patients and provide early warning functions for medical staff. The system significantly reduces the burden on healthcare providers, as it eliminates the need for manual monitoring of patients in quarantine. Moreover, it can help healthcare providers manage the COVID-19 pandemic more effectively by identifying patients who require medical attention in real time. We have validated the system and demonstrated that it is well suited to practical application, making it a promising solution for managing future pandemics. In summary, our IoT-based wearable health monitoring system has the potential to revolutionize healthcare by providing a cost-effective, remote monitoring solution for patients in quarantine. By allowing healthcare providers to monitor patients remotely in real time, the burden on medical resources is reduced, and more efficient use of limited resources is achieved. Furthermore, the system can be easily scaled to manage future pandemics, making it an ideal solution for managing the health challenges of the future.

Can stress and anxiety be assessed in African elephants (Loxodonta africana) using self-directed behaviour?

Captive African elephants used in the tourism industry face numerous welfare issues which are often stress related and linked to high numbers of tourists or human-elephant interactions. Elephant welfare is commonly assessed by quantifying faecal glucocorticoid metabolite (fGCM) concentrations, and monitoring stereotypic behaviour, which are either costly or problematic in identifying underlying causes. Self-directed behaviours (SDBs), a form of displacement activity, have been substantially linked with stress and anxiety in primates, and could be a potential but not yet utilised behavioural marker in elephants. We thus explored the usage of several trunk, tail, and body related SDBs in a group of 7 semi-captive African elephants maintained at the Knysna Elephant Park, a tourist destination offering a variety of close contact experiences. Using continuous behavioural sampling (focal following), SDBs were recorded as they occurred, along with the numbers of nearby tourists (<5 m to focal; with 3 levels: None, Low 1–5, and High >5), and the nature of the tourist interaction (5 levels: None, Feed, Touch, Walk, and Ride). We then compared SDB rates with fGCM concentrations (n = 115) determined following observations (1 day and 2 days after behavioural sampling). Data were analysed using Generalized Linear Mixed effects Models. SDB rates (p/min) significantly increased during elephant Walks (p < 0.001) and Rides (p < 0.001), indicating a correlation with their usage during potentially stressful scenarios. The Touch interaction significantly decreased SDBs (p < 0.05), whilst the Feed interaction had a non-significant effect (p > 0.05), which could indicate the element of control in terms of perceived stress is an important component in welfare, as elephants were free to move away during these interactions. Interestingly, SDBs significantly decreased when tourist numbers were High (p = < 0.05), potentially due to greater quantities of high value food. Additionally, no correlation was found between SDBs and fGCM concentrations determined on day one (p > 0.05), and day two (p > 0.05). Hence, SDBs may be correlated more with anxiety or lower-level stress, which is not significant enough to activate GC production, and may therefore act as a coping strategy utilised to maintain physiological homeostasis during anxiety-inducing situations. Additional research would benefit from coupling SDB observations with different forms of physiological assessments to better understand the internal motivations, and formally establish SDBs as a reliable, cost-effective, and non-invasive welfare index to identify stressed individuals in real time and ensure optimum husbandry.

A Survey on Medical Remote Monitoring of Multiple Physiological Parameters Based on Wireless Embedded Internet

Abstract: In rural areas the medical assessment methods have not improved enough to meet the needs and physiological parameter monitoring is been done by local monitoring system and also it has become an urgent need for the people especially for elder people and this technology enables users to concern about their personalized ,networked real time health monitor.The main resolution of this paper is that to design a global architecture with specification for medical policies which are applicable to design and implementation of an alert based medical system.so in the proposed system we are going to combine an ARM based embedded technology and a mobile technology i.e. embedded computer system and human computer interaction interface module and helps to transmit the local detection data to servers at the hospital through an internet network. Our proposed system collects the data with the help of particular sensor and display the multiple physiological parameters like body temperature , blood pressure ,blood oxygen pulse rates and also ECG signals for particular individuals in real time and Whenever the person encountered with dangerous or emergency situations we are going to get the led alarm signals. wireless technology expansion has been accelerated due to its suitability and cost effectiveness, compared with wired technology.the experimental results shows that the system has good data collection effect so that the effect of medical index collection on family health population service can beachieved]. Hence the system is easy to develop, stable in data sharing therefore its an ideal model for hospitals and public health centers.

Leveraging technology to personalize cognitive enhancement methods in aging.

As population aging advances at an increasing rate, efforts to help people maintain or improve cognitive function late in life are critical. Although some studies have shown promise, the question of whether cognitive training is an effective tool for improving general cognitive ability remains incompletely explored, and study results to date have been inconsistent. Most approaches to cognitive enhancement in older adults have taken a 'one size fits all' tack, as opposed to tailoring interventions to the specific needs of individuals. In this Perspective, we argue that modern technology has the potential to enable large-scale trials of public health interventions to enhance cognition in older adults in a personalized manner. Technology-based cognitive interventions that rely on closed-loop systems can be tailored to individuals in real time and have the potential for global testing, extending their reach to large and diverse populations of older adults. We propose that the future of cognitive enhancement in older adults will rely on harnessing new technologies in scientifically informed ways.

Privacy in the realm of Artificial Intelligence Systems Application for Remote Biometric Identification

The paper analyzes legal approaches to solving key problems of human rights implementation in the conditions of mass exploitation of artificial intelligence (AI) systems in the public space. Within the framework of the study, the emphasis is placed on the features of the legal regulation of the use of artificial intelligence systems for remote biometric identification. The use of these systems is currently only beginning to acquire a regulatory legal framework and law enforcement practice in most countries. The study analyzes several different models of legal regulation that are typical of individual countries and regions, such as the United Kingdom, the United States, China, the EU and Russia.In the UK, the use of real-time facial recognition systems in public spaces is allowed, but the set of scenarios and situations of their use is significantly limited by legislation and law enforcement practice. In the United States, both at the federal and state levels, there are no general rules that form a unified legal approach to regulating the area in question. The EC has developed a draft Regulation on Harmonized AI Rules (Artificial Intelligence Act), which is supposed to prohibit the use of AI systems for remote biometric identification of individuals in real time in public places. There is no special regulatory regulation of this sphere of public relations in the PRC. The development of these systems in China is controlled by the state, which, due to the high centralization of power, leads to the risk of human rights violations and the creation of an atmosphere of total surveillance of citizens without any legally established framework and restrictions. In Russia, the state is actively deploying these systems at the federal and regional levels in the absence of a specialized regulatory framework. Human rights are protected only by the general norms of the Constitution of the Russian Federation and legislation, law enforcement practice is mainly aimed at ensuring the interests of the state.

Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks

Controlling multiple multi-joint fish-like robots has long captivated the attention of engineers and biologists, for which a fundamental but challenging topic is to robustly track the postures of the individuals in real time. This requires detecting multiple robots, estimating multi-joint postures, and tracking identities, as well as processing fast in real time. To the best of our knowledge, this challenge has not been tackled in the previous studies. In this paper, to precisely track the planar postures of multiple swimming multi-joint fish-like robots in real time, we propose a novel deep neural network-based method, named TAB-IOL. Its TAB part fuses the top-down and bottom-up approaches for vision-based pose estimation, while the IOL part with long short-term memory considers the motion constraints among joints for precise pose tracking. The satisfying performance of our TAB-IOL is verified by testing on a group of freely swimming fish-like robots in various scenarios with strong disturbances and by a deed comparison of accuracy, speed, and robustness with most state-of-the-art algorithms. Further, based on the precise pose estimation and tracking realized by our TAB-IOL, several formation control experiments are conducted for the group of fish-like robots. The results clearly demonstrate that our TAB-IOL lays a solid foundation for the coordination control of multiple fish-like robots in a real working environment. We believe our proposed method will facilitate the growth and development of related fields.

Exploring built environment correlates of older adults’ walking travel from lifelogging images

Abstract Utilising time-stamped lifelogging images collected from a sample of 30 older adults in three neighbourhoods of Singapore, this study explores older adults’ daily walking travel patterns and their associations with neighbourhood-level built environment features. The visual lifelogging method uses a wearable camera to automatically record individual activities from a “first-person” perspective and capture novel information of environmental features encountered by individuals in real time. The findings reveal that older participants in the study areas, on average, take about 4 walking trips per day with an average trip length of about 15 minutes. Neighbourhood facilities including public open spaces, senior activity centres, and food courts are the most visited destinations while urban design features such as covered walkways, tree shades and street furniture are frequently encountered during walking trips.

Research on the effect of monitoring the individual learning status of students in English teaching on the improvement of teaching effect

In order to improve the effect of English teaching, this paper proposes a complex interactive collective behavior recognition method based on dynamic kernel density, which can more accurately identify classroom behavior patterns with complex interactions. Moreover, this paper proposes a dynamic crowd path planning method based on the emotional communication model. This method introduces the subjective factor of emotions in path planning, which can more realistically show the diversity of group path choices. At the same time, this paper combines the actual needs of English teaching to build a student status monitoring system based on group feature recognition, which can monitor the status of groups and individuals in real time. In addition, this paper introduces emotion algorithms to recognize student emotions, and combines action recognition to complete student state monitoring. Finally, this paper designs experiments to verify the performance of the system constructed in this paper. The research results show that the system constructed in this paper has certain effects and can provide theoretical reference for related research.

Next-Generation Wearable Biosensors Developed with Flexible Bio-Chips.

The development of biosensors that measure various biosignals from our body is an indispensable research field for health monitoring. In recent years, as the demand to monitor the health conditions of individuals in real time have increased, wearable-type biosensors have received more attention as an alternative to laboratory equipment. These biosensors have been embedded into smart watches, clothes, and accessories to collect various biosignals in real time. Although wearable biosensors attached to the human body can conveniently collect biosignals, there are reliability issues due to noise generated in data collection. In order for wearable biosensors to be more widely used, the reliability of collected data should be improved. Research on flexible bio-chips in the field of material science and engineering might help develop new types of biosensors that resolve the issues of conventional wearable biosensors. Flexible bio-chips with higher precision can be used to collect various human data in academic research and in our daily lives. In this review, we present various types of conventional biosensors that have been used and discuss associated issues such as noise and inaccuracy. We then introduce recent studies on flexible bio-chips as a solution to these issues.

High-Throughput Assays of Critical Thermal Limits in Insects.

Upper and lower thermal limits of plants and animals are important predictors of their performance, survival, and geographic distributions, and are essential for predicting responses to climate change. This work describes two high-throughput protocols for measuring insect thermal limits: one for assessing critical thermal minima (CTmin), and the other for assessing heat knock down time (KDT) in response to a static heat stressor. In the CTmin assay, individuals are placed in an acrylic-jacketed column, subjected to a decreasing temperature ramp, and counted as they fall from their perches using an infrared sensor. In the heat KDT assay, individuals are contained in a 96 well plate, placed in an incubator set to a stressful, hot temperature, and video recorded to determine the time at which they can no longer remain upright and move. These protocols offer advantages over commonly used techniques. Both assays are low cost and can be completed relatively quickly (~2 h). The CTmin assay reduces experimenter error and can measure a large number of individuals at once. The heat KDT protocol generates a video record of each assay and thus removes experimenter bias and the need to continuously monitor individuals in real time.

Fast Finger Vein Recognition Based on Sparse Matching Algorithm under a Multicore Platform for Real-Time Individuals Identification

Nowadays, individual identification is a problem in many private companies, but also in governmental and public order entities. Currently, there are multiple biometric methods, each with different advantages. Finger vein recognition is a modern biometric technique, which has several advantages, especially in terms of security and accuracy. However, image deformations and time efficiency are two of the major limitations of state-of-the-art contributions. In spite of affine transformations produced during the acquisition process, the geometric structure of finger vein images remains invariant. This consideration of the symmetry phenomena presented in finger vein images is exploited in the present work. We combine an image enhancement procedure, the DAISY descriptor, and an optimized Coarse-to-fine PatchMatch (CPM) algorithm under a multicore parallel platform, to develop a fast finger vein recognition method for real-time individuals identification. Our proposal provides an effective and efficient technique to obtain the displacement between finger vein images and considering it as discriminatory information. Experimental results on two well-known databases, PolyU and SDUMLA, show that our proposed approach achieves results comparable to deformation-based techniques of the state-of-the-art, finding statistical differences respect to non-deformation-based approaches. Moreover, our method highly outperforms the baseline method in time efficiency.

Analysis of the role of wind information for efficient chemical plume tracing based on optogenetic silkworm moth behavior

Many animals use olfactory information to search for feeding areas and other individuals in real time and with high efficiency. We focus on the chemical plume tracing (CPT) ability of male silkworm moths and investigate an efficient CPT strategy for an autonomous robot. In the case of flying insects, the wind direction is an important factor in CPT, because the wind carries odors amongst other environmental information. However, whether the same phenomenon occurs in the walking silkworm moth has not been investigated. Therefore, we examine how the silkworm moth uses wind information during CPT. To accurately investigate the response to the wind direction, we introduce an optogenetic approach that replaces the odor stimulation with light stimulation, allowing us to separate the ‘wind stimulus’ from the ‘odor stimulus’. We examine how the moth uses wind direction information in a biological experiment, and find that the movement speed is significantly reduced when the wind speed is relatively fast (1.0 m s−1). By implementing this phenomenon in an autonomous robot, we can improve the successful search rate over that of the conventional moth-inspired algorithm. Regarding the search time, the proposed algorithm finds the odor source faster in a low-frequency odorant emission environment, whereas the search is slower than the conventional method when the odor frequency is higher. Therefore, switching from the use of wind direction information to odor information according to the frequency with which the odor is encountered leads to efficient CPT performance.

StingRay Technology, the Exclusionary Rule, and the Future of Privacy: A Cautionary Tale

Sometime in 2017, smartphone ownership in the United States will exceed 222 million users which will be equivalent to a market penetration rate of over 85 percent. Although millions of individuals in the United States enjoy the convenience of using smartphones, it is likely that few of those citizens understand that government agencies have used those same phones to track the location of individuals in real time as well as to access the significant and previously private information stored on those devices without a warrant. The public’s general lack of awareness of the intrusiveness of current law enforcement surveillance is but one factor responsible for creating this knowledge gap. Another root of the gap however is that the judicial institutions that we entrust with protecting our rights cannot preemptively adjust constitutional doctrines to parallel technological change. Indeed, judicial institutions have typically adopted a cautious approach to adjusting constitutional doctrines to technological developments. For example, although Apple introduced the IPhone in January 2007, it took seven years for the Supreme Court to hold that government agents must obtain a warrant to search smartphones seized incident to arrest. This article argues that the widespread deployment of cell-site-simulator technology and its warrantless use by law enforcement agencies illustrates the weakness of our current structure of Fourth Amendment protections and that we cannot rely on the judicial branch to adequately protect individual privacy rights in an age of rapidly developing technology. In Part I of this article, I highlight the evolution of the use of StingRay technology in criminal investigations in the United States and the efforts by privacy rights organizations to elevate the standard of judicial scrutiny of those devices. In Part II, I examine two ground-breaking 2016 court decisions in which courts for the first time suppressed evidence obtained through the use of cell-site simulator technology. While United States v. Lambis is the first instance where a federal court suppressed stingray-related evidence, the decision of the Maryland Court of Special Appeals in State of Maryland v. Andrews, is the first state appellate decision to uphold a trial court’s CSS-related suppression order. Finally in Part III, I argue that the history of the government’s use of CSS technology demonstrates that in a common law system restricted to litigating current cases and controversies, the judicial branch standing alone cannot adequately protect individual privacy rights.

An Automatic Video-based Drowning Detection System for Swimming Pools Using Active Contours

Safety in swimming pools is a crucial issue. In this paper, a real time drowning detection method based on HSV color space analysis is presented which uses prior knowledge of the video sequences to set the best values for the color channels. Our method uses a HSV thresholding mechanism along with Contour detection to detect the region of interest in each frame of video sequences. The presented software can detect drowning person in indoor swimming pools and sends an alarm to the lifeguard rescues if the previously detected person is missing for a specific amount of time. The presented algorithm for this system is tested on several video sequences recorded in swimming pools in real conditions and the results are of high accuracy with a high capability of tracking individuals in real time. According to the evaluation results, the number of false alarms generated by the system is minimal and the maximum alarm delay reported by the system is 2.6 sec which can relatively be reliable compared to the acceptable time for rescue and resuscitation.

Making Sense of Mobile- and Web-Based Wellness Information Technology: Cross-Generational Study

BackgroundA recent trend in personal health and wellness management is the development of computerized applications or information and communication technologies (ICTs) that support behavioral change, aid the management of chronic conditions, or help an individual manage their wellness and engage in a healthier lifestyle.ObjectiveTo understand how individuals across 3 generations (young, middle-aged, and older) think about the design and use of collaborative health and wellness management technologies and what roles these could take in their lives.MethodsFace-to-face semistructured interviews, paper prototype systems, and video skits were used to assess how individuals from 3 age cohorts (young: 18-25 years; middle-aged: 35-50 years; and older: ≥65 years) conceptualize the role that health and wellness computing could take in their lives.ResultsA total of 21 participants in the 3 age cohorts took part (young: n=7; middle-aged: n=7; and older: n=7). Young adults expected to be able to actively manage the presentation of their health-related information. Middle-aged adults had more nuanced expectations that reflect their engagement with work and other life activities. Older adults questioned the sharing of health information with a larger audience, although they saw the value in 1-way sharing between family members or providing aggregated information.ConclusionsOur findings inform our suggestions for improving the design of future collaborative health and wellness applications that target specific age groups. We recommend that collaborative ICT health applications targeting young adults should integrate with existing social networking sites, whereas those targeting middle-aged and older adults should support small social networks that rely on intimate personal relationships. Systems that target middle-aged adults should support episodic needs, such as time-sensitive, perhaps intermittent, goal setting. They should also have a low barrier to entry, allowing individuals who do not normally engage with the Internet to participate with the application for the specific purposes of health engagement. Collaborative ICT health applications targeting older adults should allow discreet 1-way sharing, and also support sharing of information in aggregate with others’ data. These systems should also provide mechanisms to preselect recipients of different kinds of data, or to easily direct specific information to individuals in real time.

Tracking children's mental states while solving algebra equations

Behavioral and function magnetic resonance imagery (fMRI) data were combined to infer the mental states of students as they interacted with an intelligent tutoring system. Sixteen children interacted with a computer tutor for solving linear equations over a six-day period (days 0-5), with days 1 and 5 occurring in an fMRI scanner. Hidden Markov model algorithms combined a model of student behavior with multi-voxel imaging pattern data to predict the mental states of students. We separately assessed the algorithms' ability to predict which step in a problem-solving sequence was performed and whether the step was performed correctly. For day 1, the data patterns of other students were used to predict the mental states of a target student. These predictions were improved on day 5 by adding information about the target student's behavioral and imaging data from day 1. Successful tracking of mental states depended on using the combination of a behavioral model and multi-voxel pattern analysis, illustrating the effectiveness of an integrated approach to tracking the cognition of individuals in real time as they perform complex tasks.