Uncontrolled Environment Research Articles

Types of anomalies in two-dimensional video-based gait analysis in uncontrolled environments.

Two-dimensional video-based pose estimation is a technique that can be used to estimate human skeletal coordinates from video data alone. It is also being applied to gait analysis and in particularly, due to its simplicity of measurement, it has the potential to be applied to gait analysis of large populations. However, it is considered difficult to completely homogenize the environment and settings during the measurement of large populations. Therefore, it is necessary to appropriately deal with technical errors that are not related to the biological factors of interest. In this study, by analyzing a large cohort database, we have identified four major types of anomalies that occur during gait analysis using OpenPose in uncontrolled environments: anatomical, biomechanical, and physical anomalies and errors due to estimation. We have also developed a workflow for identifying and correcting these anomalies and confirmed that this workflow is reproducible through simulation experiments. Our results will help obtain a comprehensive understanding of the anomalies to be addressed during pre-processing for 2D video-based gait analysis of large populations.

Sign Language Recognition using Deep Neural Network

Abstract—Sign language recognition is an attractive research field with a wide range of applications including telesurgery techniques. Another important application of hand gesture recognition is the translation of sign language, which is a complicated structured form of hand gestures. In sign language, the fingers’ configuration, the hand’s orientation, and the hand’s relative position to the body are the basis of structured expressions. The importance of hand gesture recognition has increased due to the prevalence of touchless applications and the rapid growth of the hearing-impaired population. However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and gesture sequence modeling. In this paper, a novel system is proposed for dynamic hand gesture recognition using multiple deep learning architectures for hand segmentation, local and global feature representations, and sequence feature globalization and recognition. The proposed system is evaluated on a very challenging dataset, which consists of dynamic hand gestures performed by subjects in an uncontrolled environment. The results show that the proposed system outperforms state-of- the-art approaches, demonstrating its effectiveness.

A Corneal Surface Reflections-Based Intelligent System for Lifelogging Applications

Corneal Surface Reflections, or reflections on our eye-surface, have been shown as a valid and more socially acceptable source of information for passive lifelogging applications by prior work. However, automatic analysis of corneal surface reflections from a single RGB camera to support passive lifelogging is not extensively investigated in prior work. To address this, we developed a synthetic and self-supervised learning-based two-stage pipeline of deep learning models to detect objects in these reflections. Our prototype only consists a single RGB camera looking into the eye. We collected data from different users in uncontrolled environments using the prototype and trained our system to detect multiple classes of objects present in a typical office environment. We then evaluated our model in partially-controlled and in-the-wild scenarios. In addition, based on the findings from a follow up user study and prior work, we discuss strengths and weaknesses of our system and using corneal surface reflections for passive lifelogging. Finally, we opensource our source codes and trained checkpoints.

Vision-Based System for Measuring the Diameter of Wood Logs

Detecting and measuring objects with vision-based systems in uncontrolled environments is a difficult task that today, thanks to the development of increasingly advanced artificial intelligence-based techniques, can be solved with greater ease. In this context, the paper proposes a novel approach for the vision-based measurement of objects in uncontrolled environments using a specific type of Convolutional Neural Network (CNN) named YOLO (You Only Look Once) and a Direct Linear Transformation (DLT) process. The case study concerned designing a novel vision-based system for measuring the diameter of wood logs cut and loaded onto trucks. This problem has been occurring in the Swedish forestry industry. In fact, this operation is not carried out with computer vision algorithms because of the high variability of environmental conditions caused by the changing position of the sun, weather conditions, and the variability of truck positioning. To solve this problem, the YOLO network is proposed to locate logs while attempting to maintain a high Intersection over Union (IoU) value for the correct estimation of log size. Furthermore, in order to obtain accurate measurements, the DLT is used to convert into world coordinates the dimensions of the logs themselves. The proposed CNN-based solution is described after briefly introducing today’s methodologies adopted for wood bundle analysis. Particular attention is paid to both the training and the calibration steps. Results report that for 80% of cases, the error reported has been smaller than 4 cm, representing only 8% of the measurement, considering a mean log diameter for the application of 50 cm.

Smart Water Meter Based on Deep Neural Network and Undersampling for PWNC Detection

Having become aware of how limited all natural resources are, the water leakage problem in piping systems has become a stimulating topic. This problem increased over the last few years even though innovative tools and techniques appeared in the literature and in the consumer market. Identifying water leaks at the nearest point, the household level, is still an unsolved problem because most water meters are mechanical and, therefore, cannot detect leaks. While the issue is not important for water service providers since consumption is charged to the user, the resolution is crucial due to the increasingly relevant concern of saving natural resources. The detection of small but continuous leaks of drinking water in domestic systems is addressed in this work. Machine learning approaches enabled image processing techniques also in uncontrolled environments, overcoming the classical methods but introducing new challenges like power consumption. Using a combination of Convolutional Neural Networks (CNNs) and Long Short–Term Memory Networks (LSTM) within an adaptive under–sampling strategy, it is possible to process the images captured from the mechanical water meter dial and identify the Period With Null Consumption (PWNC) or the consumption class. The presented solution can classify the water flow into four different classes, and, in the case of absence or small flow, its function becomes to detect leakages. Analyzing images from a mechanical water meter quadrant, it has been possible to identify PWNC and detect small water leakages in the domestic environment under common consumer flow profiles. In addition to the confusion matrices, the synthetic parameters of Sørensen–Dice coefficient (DSC) and Jaccard Index have been utilized and presented to quantify the performance of the proposed DNN. The conducted experiments on static and dynamic water flow demonstrated the applicability of this approach and the possibility of an increase in PWNC identification thanks to the adaptative increase in the sampling time. Moreover, the reduction of sampling time allows for the reduction of computational load and power consumption in embedded scenarios where limited energy is available.

An Effective Threat Detection Framework for Advanced Persistent Cyberattacks

Recently, with the normalization of non-face-to-face online environments in response to the COVID-19 pandemic, the possibility of cyberattacks through endpoints has increased. Numerous endpoint devices are managed meticulously to prevent cyberattacks and ensure timely responses to potential security threats. In particular, because telecommuting, telemedicine, and tele-education are implemented in uncontrolled environments, attackers typically target vulnerable endpoints to acquire administrator rights or steal authentication information, and reports of endpoint attacks have been increasing considerably. Advanced persistent threats (APTs) using various novel variant malicious codes are a form of a sophisticated attack. However, conventional commercial antivirus and anti-malware systems that use signature-based attack detection methods cannot satisfactorily respond to such attacks. In this paper, we propose a method that expands the detection coverage in APT attack environments. In this model, an open-source threat detector and log collector are used synergistically to improve threat detection performance. Extending the scope of attack log collection through interworking between highly accessible open-source tools can efficiently increase the detection coverage of tactics and techniques used to deal with APT attacks, as defined by MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK). We implemented an attack environment using an APT attack scenario emulator called Carbanak and analyzed the detection coverage of Google Rapid Response (GRR), an open-source threat detection tool, and Graylog, an open-source log collector. The proposed method expanded the detection coverage against MITRE ATT&CK by approximately 11% compared with that conventional methods.

Nanostructured wearable electrochemical and biosensor towards healthcare management: a review.

In recent years, there has been a rapid increase in demand for wearable sensors, particularly these tracking the surroundings, fitness, and health of people. Thus, selective detection in human body fluid is a demand for a smart lifestyle by quick monitoring of electrolytes, drugs, toxins, metabolites and biomolecules, proteins, and the immune system. In this review, these parameters along with the main features of the latest and mostly cited research work on nanostructured wearable electrochemical and biosensors are surveyed. This study aims to help researchers and engineers choose the most suitable selective and sensitive sensor. Wearable sensors have broad and effective sensing platforms, such as contact lenses, Google Glass, skin-patch, mouth gourds, smartwatches, underwear, wristbands, and others. For increasing sensor reliability, additional advancements in electrochemical and biosensor precision, stability in uncontrolled environments, and reproducible sample conveyance are necessary. In addition, the optimistic future of wearable electrochemical sensors in fields, such as remote and customized healthcare and well-being is discussed. Overall, wearable electrochemical and biosensing technologies hold great promise for improving personal healthcare and monitoring performance with the potential to have a significant impact on daily lives. These technologies enable real-time body sensing and the communication of comprehensive physiological information.

Deep Transfer Learning Approach for Robust Hand Detection

Human hand detection in uncontrolled environments is a challenging visual recognition task due to numerous variations of hand poses and background image clutter. To achieve highly accurate results as well as provide real-time execution, we proposed a deep transfer learning approach over the state-of-the-art deep learning object detector. Our method, denoted as YOLOHANDS, is built on top of the You Only Look Once (YOLO) deep learning architecture, which is modified to adapt to the single class hand detection task. The model transfer is performed by modifying the higher convolutional layers including the last fully connected layer, while initializing lower non-modified layers with the generic pretrained weights. To address robustness issues, we introduced a comprehensive augmentation procedure over the training image dataset, specifically adapted for the hand detection problem. Experimental evaluation of the proposed method, which is performed on a challenging public dataset, has demonstrated highly accurate results, comparable to the state-of-the-art methods.

Unconstrained Gender Recognition from Periocular Region Using Multiscale Deep Features

The gender recognition problem has attracted the attention of the computer vision community due to its importance in many applications (e.g., surveillance and human–computer interaction [HCI]). Images of varying levels of illumination, occlusion, and other factors are captured in uncontrolled environments. Iris and facial recognition technology cannot be used on these images because iris texture is unclear in these instances, and faces may be covered by a scarf, hijab, or mask due to the COVID-19 pandemic. The periocular region is a reliable source of information because it features rich discriminative biometric features. However, most existing gender classification approaches have been designed based on hand-engineered features or validated in controlled environments. Motivated by the superior performance of deep learning, we proposed a new method, PeriGender, inspired by the design principles of the ResNet and DenseNet models, that can classify gender using features from the periocular region. The proposed system utilizes a dense concept in a residual model. Through skip connections, it reuses features on different scales to strengthen discriminative features. Evaluations of the proposed system on challenging datasets indicated that it outperformed state-of-the-art methods. It achieved 87.37%, 94.90%, 94.14%, 99.14%, and 95.17% accuracy on the GROUPS, UFPR-Periocular, Ethnic-Ocular, IMP, and UBIPr datasets, respectively, in the open-world (OW) protocol. It further achieved 97.57% and 93.20% accuracy for adult periocular images from the GROUPS dataset in the closed-world (CW) and OW protocols, respectively. The results showed that the middle region between the eyes plays a crucial role in the recognition of masculine features, and feminine features can be identified through the eyebrow, upper eyelids, and corners of the eyes. Furthermore, using a whole region without cropping enhances PeriGender’s learning capability, improving its understanding of both eyes’ global structure without discontinuity.

Portable heat transfer measurement system mimicking human thermal sensation

In this study, we developed a portable heat transfer measurement module which measures the heat transfer characteristics of a contact object by mimicking the human thermal sensation mechanism. By applying a structure which improves the contact interface characteristics and allows the module and an object to remain in contact with a constant force, the developed portable heat transfer measurement module can reliably measure the thermal characteristics of a contact object within one second in an uncontrolled environment. We discriminated 20 fabrics using their heat transfer characteristics which was measured by the developed module in a portable environment. We observed that the heat transfer characteristics measured by our module had a high correlation ( R 2 value = 0.949) with the human perceived coolness, obtained from a psychological tactile survey result on the same 20 fabrics. Our module can effectively represent the numerical value predicting the warmth/coolness which a person would perceive from a contact object, so it may be possible to apply the module to biomedical and robotic applications which require the measurement and the classification of the heat transfer characteristics of contact objects. • ⋅ Portable heat transfer measurement module is developed mimicking the human thermal sensation mechanism. • ⋅ The developed portable heat transfer measurement module reliably measured the heat transfer feature of a contact object within one second in a portable environment. • ⋅ The measured heat transfer feature had a high correlation ( R 2 value = 0.949) with the human perceived coolness obtained from a psychological tactile survey. • ⋅ The portable heat transfer measurement module presents the numerical value predicting the warmth/coolness which a person would perceive from a contact object.

MAUTH: Continuous User Authentication Based on Subtle Intrinsic Muscular Tremors

Continuous authentication is viewed to be increasingly important for mobile devices, which store a wide range of private data and sensitive information of users. Traditional continuous authentication methods need user inputs (e.g. typing, sliding). In this work, we present MAUTH, a zero-effect continuous authentication scheme for mobile devices. With the built-in motion sensors on commercial off-the-shelf (COTS) devices, MAUTH can continuously extract, classify and verify the unique tremor features of users on how their body intrinsically shakes during the normal use of such devices. As a result, it is extremely difficult if not impossible to reproduce the same set of tremors as individuals differ in their muscle development. We implement MAUTH as a software on Android-based smartphones, which demonstrates that MAUTH is light-weight and unobtrusive to its users. We conduct extensive real-world experiments and trace-driven simulations in controlled and uncontrolled environments on 21 volunteers. The results show that MAUTH is difficult to counterfeit and achieves a low average false positive rate (FPR) of 6.73% under real-world spoofing attacks. Moreover, MAUTH is comfortable to use and can achieve a low average false negative rate (FNR) of 2.2% during uncontrolled and continuous usage of devices, leveraging isolation-forest-based classifiers trained with only 40 training samples.

Chinese Face Dataset for Face Recognition in an Uncontrolled Classroom Environment

Chinese Face Dataset for Face Recognition in an Uncontrolled Classroom Environment

Acoustic Feedback Noise Cancellation in Hearing Aids Using Adaptive Filter

To enhance speech intelligibility for people with hearing loss, hearing aids will amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. In general, an uncontrolled environment may contain degradation components like background noise, speech from other speakers etc. along with required speech components. In this paper, we implement adaptive filtering design for acoustic feedback noise cancellation in hearing aids. The adaptive filter architecture has been designed using normalized least mean square algorithm. By using adaptive filters both filter input coefficients are changeable during run-time and reduce noise in hearing aids. The proposed design is implemented in matlab and the simulations shows that the proposed architecture produces good quality of speech, accuracy, maintain stable steady state. The proposed design is validated with parameters like Noise Distortion, Perceptual Evaluation of Speech Quality, Signal to Noise Ratio, and Speech Distortion. The feedback canceller is implemented in MATLAB 9.4 simulink version release name of R2018a is used for validation with Echo Return Loss Enhancement (ERLE). The ERLE of the NMLS is reduced when the filter order is increases.Around 10% of the power spectrum density (PSD) is less when compared with existing designs.

An exploratory study to understand how people use Twitter to share experiences or information about spinal stenosis

BackgroundSpinal stenosis is a narrowing of the spinal canal that may compress neurological tissues resulting in pain and disability. Although previous qualitative studies have solicited data regarding the life experience of patients with spinal stenosis or their opinions on relevant non-surgical treatments, their data was collected from participants in a controlled setting. Therefore, it remains unclear whether patients’ or caregivers’ concerns/opinions about spinal stenosis would be different in a non-experimental environment. Since Twitter is a popular online platform for people to share information and interact, it may reveal people’s thoughts and attitudes about spinal stenosis. This study aimed to identify tweets that are related to spinal stenosis on Twitter, and to categorize them into common themes.MethodsA social media monitoring and analysis software program (TalkWalker) was used to search relevant tweets using the keywords 'spinal stenosis' and 'stenosis' between 29 May 2019 and 24 June 2020. Two independent reviewers screened and conducted content analysis of the tweets and classified the tweets into different themes.ResultsOf 510 identified tweets, 362 tweets met the selection criteria. Five themes were identified: (1) compromised physical, psychological, and social wellbeing (n = 173); (2) diverse treatment options (n = 69); (3) coping strategies (n = 30); (4) dissemination of scientific information (n = 86); and (5) health policy (n = 4). Most of the tweets revealed negative impacts of spinal stenosis on patients' physical and psychosocial wellbeing. People with spinal stenosis shared their experiences and sought helps from others, while some people used Twitter to disseminate relevant information and research findings.ConclusionsThis is the first study exploring Twitter using an online analytical tool to identify themes related to spinal stenosis. The approach not only helps understand people’s concerns about spinal stenosis in an uncontrolled environment, but also can be adopted to monitor influences of diseases or public health education on Twitter users.

Remote Electronic Voting in Uncontrolled Environments: A Classifying Survey

Remote electronic voting, often called online or Internet voting, has been subject to research for the last four decades. It is regularly discussed in public debates, especially in the context of enabling voters to conveniently cast their ballot from home using their personal devices. Since these devices are not under the control of the electoral authority and could be potentially compromised, this setting is referred to as an “uncontrolled environment” for which special security assumptions have to be considered. This paper employs general election principles to derive cryptographic, technical, and organizational requirements for remote electronic voting. Based on these requirements, we have extended an existing methodology to assess online voting schemes and develop a corresponding reference attacker model to support the preparation of tailored protection profiles for different levels of elections. After presenting a broad survey of different voting schemes, we use this methodology to assess and classify those schemes comparatively by leveraging four election-specific attacker models.

GCR‐Net: A deep learning‐based bare hand detection and gesticulated character recognition system for human‐computer interaction

SummaryPrecisely detecting bare hands and recognizing the characters are two major stages in gesticulated character recognition systems. It is very challenging to implement them in an uncontrolled environment. Additional variations, particularly (i) background feature domination (BFD) effect and motion blur in detection, (ii) gesturing style, pattern, and case sensitivity in recognition, make the system more complex. To address these challenges, a gesticulated character recognition (GCR‐Net) model is designed. To detect the bare hand precisely, a pixel‐wise segmentation approach, HandSNet, is presented, which is able to overcome the BFD effect. To handle the motion blur in the frames, a tracking module comprised of a point‐tracker and Kalman filter is applied. To reduce the computational time, a mini‐SqueezeNet network is designed, which is used in HandSNet and recognition models as the backend network. It has 0.39 million parameters only. Four separate deep convolutional neural networks (DCNNs) are connected with the network section module at the recognition end. This network selection module activates one DCNN at a time to recognize the gesticulated characters accurately. The proposed GCR‐Net reduces the complexity between similar characters and provides a high precision rate compared to the existing approaches.

Image Segmentation for Human Skin Detection

Human skin detection is the main task for various human–computer interaction applications. For this, several computer vision-based approaches have been developed in recent years. However, different events and features can interfere in the segmentation process, such as luminosity conditions, skin tones, complex backgrounds, and image capture equipment. In digital imaging, skin segmentation methods can overcome these challenges or at least part of them. However, the images analyzed follow an application-specific pattern. In this paper, we present an approach that uses a set of methods to segment skin and non-skin pixels in images from uncontrolled or unknown environments. Our main result is the ability to segment skin and non-skin pixels in digital images from a non-restrained capture environment. Thus, it overcomes several challenges, such as lighting conditions, compression, and scene complexity. By applying a segmented image examination approach, we determine the proportion of skin pixels present in the image by considering only the objects of interest (i.e., the people). In addition, this segmented analysis can generate independent information regarding each part of the human body. The proposed solution produces a dataset composed of a combination of other datasets present in the literature, which enables the construction of a heterogeneous set of images.

Remarkably reproducible psychological (memory) phenomena in the classroom: some evidence for generality from small-N research

BackgroundMainstream psychology is experiencing a crisis of confidence. Many of the methodological solutions offered in response have focused largely on statistical alternatives to null hypothesis statistical testing, ignoring nonstatistical remedies that are readily available within psychology; namely, use of small-N designs. In fact, many classic memory studies that have passed the test of replicability used them. That methodological legacy warranted a retrospective look at nonexperimental data to explore the generality of the reported effects.MethodVarious classroom demonstrations were conducted over multiple semesters in introductory psychology courses with typical, mostly freshman students from a predominantly white private Catholic university in the US Midwest based on classic memory experiments on immediate memory span, chunking, and depth of processing.ResultsStudents tended to remember 7 ± 2 digits, remembered more digits of π following an attached meaningful story, and remembered more words after elaborative rehearsal than after maintenance rehearsal. These results amount to replications under uncontrolled classroom environments of the classic experiments originally conducted largely outside of null hypothesis statistical testing frameworks.ConclusionsIn light of the ongoing replication crisis in psychology, the results are remarkable and noteworthy, validating these historically important psychological findings. They are testament to the reliability of reproducible effects as the hallmark of empirical findings in science and suggest an alternative approach to commonly proffered solutions to the replication crisis.

Distance learning and its relation to medical education in the present times

This scientific text addresses the evolution of distance education, highlighting the crucial role of information and communication technologies (ICT) in virtual education. The COVID-19 pandemic accelerated the transition to distance education, especially in Latin America, although challenges are faced due to the lack of infrastructure and Internet access in remote areas. The text introduces key concepts such as distance and virtual education, as well as ICTs. It explores pedagogical theories, such as cyberculture and connectivism, that support online teaching. Connectivism is highlighted as a theory that emphasizes the importance of connections and learning in changing and uncontrolled environments. Suggestions for applying connectivism in medical education are provided, including the use of blogs, social networking, and the creation of online communities. The text also emphasizes the need to constantly adapt teaching strategies to keep up with advances in the field of medicine and health sciences. Several tools applied to virtual teaching are described, such as the Zoom and Google Meet videoconferencing platforms, as well as the Moodle and Google Classroom learning platforms. In addition, design and gamification tools are mentioned, along with the importance of clouds for online storage and collaboration.

Recognition of the condition of construction materials using small datasets and handcrafted features

We propose using handcrafted features extracted from small datasets to classify the conditions of the construction materials. We hypothesize that features such as the color, roughness, and reflectance of a material surface can be used to identify details of the material. To test the hypothesis, we have developed a pre-trained model to classify material conditions based on reflectance, roughness and color features extracted from image data collected in a controlled (lab) environment. The knowledge learned in the pre-trained model is finally transferred to classify material conditions from a construction site (i.e., an uncontrolled environment). To demonstrate the proposed method, 80 data points were produced from the images collected under a controlled environment and used to develop a pre-trained model. The pre-trained model was re-trained to adapt to the real construction environment using 33 new data points generated through a separate process using images collected from a construction site. The pre-trained model achieved 93%; after retraining the model with the data from the actual site, the accuracy had a small decrease as expected, but still was promising with an 83% accuracy.