Subtle Actions Research Articles

High-Performance Stretchable Strain Sensors Based on Auxetic Fabrics for Human Motion Detection.

Wearable strain sensors play a pivotal role in real-time human motion detection and health monitoring. Traditional fabric-based strain sensors, typically with a positive Poisson's ratio, face challenges in maintaining sensitivity and comfort during human motion due to conflicting resistance changes in different strain directions. In this work, high-performance stretchable strain sensors are developed based on graphene-modified auxetic fabrics (GMAF) for human motion detection in smart wearable devices. The proposed GMAF sensors, with a negative Poisson's ratio achieved through commercially available warp-knitting technology, exhibit an 8-fold improvement in sensitivity compared to conventional plain fabric sensors. The unique auxetic fabric structure enhances sensitivity by synchronizing resistance changes in both wale and course directions. The GMAF sensors demonstrate excellent washability, showing only slight degradation in auxeticity and an acceptable increase in resistance after 10 standard wash cycles. The GMAF sensors maintain stability under different strain levels and various motion frequencies, emphasizing their dynamic performance. The sensors exhibit superior conformability to joint movements, which effectively monitor a full range of motions, including joint bending, sports activities, and subtle actions like coughing and swallowing. The research underscores a promising approach to achieve industrial-scale production of wearable sensors with improved performance and comfort through fabric structure design.

Machine Learning-Based Human Movements Mimicking System for Animation and Virtual Reality

A trend, which can be noticed over the last several years, is the increasing interest to the machine learning methods in the animation and virtual reality (VR) industries to process the human motion data. The present abstract studies the possible contribution of human like machine learning simulations on human motions, which aim to improve animation and motion synthesis personalization. In this research, we propose a novel framework that utilizes camera-captured human motion data. This technique, which takes advantage of comes of motion data to simulate these animated characters movements in real time, will allow animators to create more creature-like animated figures with subtle actions as seen in real life. Additionally, in virtual reality, it links users’ movements with their avatars so the whole interaction is more engaging and realistic.

How Do Students Receive Help from Teachers? Initiating Assistance in Small Group Classroom Interactions

Abstract How do teachers decide when and how to help their students if not explicitly asked to do so? Based on conversation analysis of 14 h of video-recorded small group interactions in secondary schools, we discovered that teachers and students orient to subtle actions built through embodied conduct, to decide whether or not assistance is needed. We also found that problem-solving can be initiated without disturbing students’ progress and that the teachers’ movement creates opportunities for students to recruit their assistance in small group classroom interaction. The study contributes to conversation analytic and educational research by showing that embodiment is crucial for receiving and offering assistance in classrooms. In addition, this article provides insights into how experienced teachers manage the subtle and negotiated aspects of supporting learning in classrooms, an important and recurring part of classroom management that has received little attention in previous research.

AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time

Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision. To capture the subtle actions of humans for complex behavior analysis, whole-body pose estimation including the face, body, hand and foot is essential over conventional body-only pose estimation. In this article, we present AlphaPose, a system that can perform accurate whole-body pose estimation and tracking jointly while running in realtime. To this end, we propose several new techniques: Symmetric Integral Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and Pose Aware Identity Embedding for jointly pose estimation and tracking. During training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain knowledge distillation to further improve the accuracy. Our method is able to localize whole-body keypoints accurately and tracks humans simultaneously given inaccurate bounding boxes and redundant detections. We show a significant improvement over current state-of-the-art methods in both speed and accuracy on COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose estimation dataset. Our model, source codes and dataset are made publicly available at https://github.com/MVIG-SJTU/AlphaPose.

Influence of Workplace Microaggressions on Engineering Female Faculty Motivation to do Research

Negative and often unconscious beliefs about marginalised groups, including women and people of colour, sometimes manifest in discriminatory and degrading slights called microaggressions. Since most often microaggressions are in the form of subtle actions, unobtrusive comments, or humorous gestures, they are frequently overlooked as innocent and harmless, specifically to bystanders. However, their adverse effects on those on the receiving end are anything but innocuous, even if perpetrators are utterly unaware of their harmful comments or behaviours. Minorities and marginalized individuals often find microaggressions more harmful than blatant racism and discrimination. Six hundred and eleven STEM (Science, Technology, Engineering, Math) faculty from ten USA universities completed an online survey in the spring of 2021, of which 39% self-identified as Underrepresented Minority, URM, faculty. This study revealed that on average, URM women were 50% more susceptible to gender microaggressions, which correlated negatively with autonomy (having choice) and competence (being capable and effective), and positively with amotivation (lack of motivation). Case in point, 38% of them believed their opinions were overlooked in a group discussion because of their gender. Women with intersecting identities, such as women of colour, experienced both forms of gender and racial/ethnic microaggressions. They have experienced being ignored at work, being treated differently, and their opinion being overlooked based on their gender and/or their race/ethnicity. While detecting bias and microaggression and acknowledging their occurrence is crucial, taking deliberate and precise actions to disrupt and prevent them from re-occurring is even more pivotal. By realising the prevalence of discrimination and microaggressions towards underrepresented minority female faculty, and sharing insights into the complex and overarching race, ethnic, and gender relations among other social constructs, this study deepens our understanding of the challenges and barriers that this group has to grapple with. By adopting and creating effective institutional policies and professional training in support of diversity, inclusion, and cultural competency we can improve the experiences of URM faculty and positively impact their motivation and productivity.

Causing trouble and being transmissible: COVID-19 survivors' experiences of stigma and discrimination in South Korea.

The stigma associated with coronavirus disease (COVID-19) is relatively neglected in policies for handling the disease. Stigmatization occurs only within specific social contexts in local societies. This study aims to examine COVID-19 survivors' experiences of social stigma and discrimination in South Korea in the first 2 years of the pandemic. Semi-structured interviews were conducted. Of 52 participants, 45 reported that they had to cope with stigma and discrimination in their intimate social relationships, workplaces, and children's schools, ranging from subtle actions to job loss. Sexual minorities who were involved in mass disease transmission in the early part of the pandemic experienced a higher level of stigmatization. The stigmatization dealt with in this study was related to two themes: survivors' sense of causing trouble and possibility of transmission. By intertwining this stigma with the experiences of public health measures through the voices of survivors, this study reveals the local context of East Asia in terms of culture-specific aspects of COVID-19-related stigma.

INVOLVEMENT OF TEENAGERS IN THE BEHAVIOR OF BULLYING & CYBER VIOLENCE

The terms of bullying and cyberbullying have been more and more present in the educational systems all over the world. The changing social life and the increase of technological resources, has brought to different forms of aggressiveness within children and teenagers. Bullying is a form of aggressive behavior in which someone intentionally and repeatedly assaults, harasses, harasses or intimidates another person. Bullying can be physical, verbal or in the form of more subtle actions. It involves an imbalance of power between two people, most often children or teenagers. Cyberbullying means bullying through the use of digital technologies. It can happen on social networks, messaging platforms, gaming platforms and mobile phones. It is a repeated behavior in order to scare, anger or humiliate those targeted. Face-to-face bullying and online bullying can occur simultaneously. Cyberbullying leaves recordings that can prove useful and provide the evidence needed to end the aggression. Schools, have to find new ways to fight this trend. The present article, shows a brief theoretical approach on the main concepts, and deeper research on the situation of cyber violence in public schools from Iraq, Baghdad.

Definitional boundaries of discrimination: Tools for deciding what constitutes discrimination (and what doesn't)

AbstractWhat counts as discrimination? Sometimes an event has to be a deliberate act of hate before it is described as discrimination. Sometimes “discrimination” can include much more subtle actions (e.g., microaggressions). There is good evidence that “what counts” as discrimination is mired in controversy, uncertainty, or ambivalence. We present a novel approach that bridges sociocultural and social cognitive accounts of “discrimination.” Definitional boundaries of discrimination are the widely shared, common sense rules that people deploy when arguing whether an event constitutes discrimination or not (e.g., an emphasis on the personality of the alleged perpetrator; the importance of intention; and claims that the problem of discrimination is small and/or mostly limited to the past). These rules are culturally situated but also deployed by individuals in specific local contexts. We introduce a 15‐item measure of the extent to which participants are deploying broader or narrower definitional boundaries of discrimination (DBDs). We demonstrate that the measure has good convergent and discriminant validity (Study 1); that participants who are deploying narrow DBDs are less likely to make attributions to discrimination (Study 2); that participants' DBDs predict judgments for some intergroup contexts but not others (Study 3), and that participants who identify as racialized majorities have significantly narrower racism DBDs compared to participants who identify as racialized minorities (Study 4). We conclude with suggestions about how DBDs could be used in future research.

MIAM: Motion information aggregation module for action recognition

In the field of action recognition based on RGB videos, it is infeasible to train deep networks on dozens or hundreds of frames because of limits on computational complexity and memory. Previous works commonly adopted a sparse sampling strategy, which unfortunately leads to missing crucial frames and insufficient modelling for short-range motions. In this letter, an effective motion information aggregation module (MIAM) that utilises convolutional neural networks to aggregate motion information from multiple frames into one single frame is proposed. This allows to train deep networks end-to-end with densely sampled frames efficiently. The MIAM enables the model to gather motion information at every instant of action, which avoids missing subtle movements. Experiments on the NTU RGB+D 60 and NTU RGB+D 120 datasets verify that the MIAM significantly improves the recognition accuracy with very limited extra computational cost and exhibits unique advantages for recognising subtle actions.

Spatial-Temporal Information Aggregation and Cross-Modality Interactive Learning for RGB-D-Based Human Action Recognition

The RGB-D-based human action recognition is gaining increasing attention because the different modalities can provide complementary information. However, the recognition performance is still not satisfactory due to the limited ability to learn spatial-temporal feature and insufficient inter-model interaction. In this paper, we propose a novel approach for RGB-D human action recognition by aggregating spatial-temporal information and implementing cross-modality interactive learning. Firstly, a spatial-temporal information aggregation module (STIAM) is proposed to utilizes sample convolutional neural networks (CNNs) to aggregate the spatial-temporal information in entire RGB-D sequence into lightweight representations efficiently. This allows the model to extract richer spatial-temporal features with limited extra memory and computational cost. Secondly, a cross-modality interactive module (CMIM) is proposed to fully fuse the multi-modal complementary information. Moreover, a multi-modal interactive network (MMINet) is constructed for RGB-D-based action recognition by embeding the above two modules into the two-stream CNNs. In order to verify the universality of our approach, two backbones are deployed in the two-stream architecture, successively. Ablation experiments demonstrate that the proposed STIAM can bring significant improvement in recognizing actions. CMIM can further play the advantages of complementary features of multiple modalities. Extensive experiments on NTU RGB+D 60, NTU RGB+D 120 and PKU-MMD datasets proved the effectiveness of the proposed approach. The proposed approach achieves an accuracy of 94.3% and 96.5% for cross-subject and cross-view on NTU RGB+D 60, 91.7% and 92.6% for cross-subject and cross-setup on NTU RGB+D 120, 93.6% and 94.2% for cross-subject and cross-view on PKU-MMD datasets, which are the state-of-the-art performance. Further analysis denotes that our approach has advantages in recognizing subtle actions.

Is there a violation of the principle of double trial (Non Bis In Ídem) in organized crime offenses and their offenses, fines?

In today's social and legal situation, organized crime or crime is a phenomenon that occurs repeatedly, but under different forms or modalities that range from peaceful but subtle actions and others, violent and open. This has occurred especially in the phenomenon involving drug trafficking, human trafficking and arms trafficking. Being, in addition, criminal modalities that in themselves and by their very nature are very dangerous. The foregoing translates into the importance and significance of implementing at the state level and at the global level, criminal policies aimed at combating organized crime; however, these criminal policies must be respectful of due process and the basic guarantees that inform it, including, the principle of prohibition of double trial and punishment, known as non bis in idem, constituting the object of our work. The methods used from the theoretical level were the logical historical and the synthetic analytical, and from the empyrean the document analysis, and the analytical exegetical.

Forum theatre as a tool for unveiling gender issues in science, technology, engineering and mathematics (STEM) working environments

Abstract. Gender affects all aspects of life, and the working and learning environments of science, technology, engineering and geosciences present no exception. Gender issues concerning the access, permanence and ascension of women in exact sciences and Earth sciences careers in general are related to a variety of causes. The underrepresentation of women in science communications, sexual or moral harassment caused by professors and colleagues during undergraduate and graduate ages or the overloading of girls, when compared to boys, with housework during early school ages are some examples mentioned in the literature. In other words, the gender imbalance in science and technology careers may be seen as the result of a series of structured oppression suffered by women of all ages. In this context, we propose the development of an education package that is designed to understand these processes at different levels. One of the tools of this package is known as the “Theatre of the Oppressed”. Elaborated on by Augusto Boal in the 1970s, the Theatre of the Oppressed uses theatre techniques as a means of promoting social and political changes. Usually, a scene takes place that reveals a situation of oppression. The audience become what is called “spect-actors”, where they become active by exploring, showing and transforming the reality in which they are living. In the context of gender issues in exact sciences careers, the students can stage situations that reveal the subtle actions of power relations that usually put women in subservient positions. Our experience showed that, even though the acting is based on fiction, the spectators learn a great deal from the enactment because the simulation of real-life situations, problems and solutions stimulates the practice of resisting oppression in reality from within a setting that offers a safe space to practise making a change.

Multiple stressors in small streams in the forestry context of Fennoscandia: The effects in time and space

In this paper we describe how forest management practices in Fennoscandian countries, namely Sweden and Finland, expose streams to multiple stressors over space and time. In this region, forestry includes several different management actions and we explore how these may successively disturb the same location over 60–100 year long rotation periods. Of these actions, final harvest and associated road construction, soil scarification, and/or ditch network maintenance are the most obvious sources of stressors to aquatic ecosystems. Yet, more subtle actions such as planting, thinning of competing saplings and trees, and removing logging residues also represent disturbances around waterways in these landscapes. We review literature about how these different forestry practices may introduce a combination of physicochemical stressors, including hydrological change, increased sediment transport, altered thermal and light regimes, and water quality deterioration. We further elaborate on how the single stressors may combine and interact and we consequently hypothesise how these interactions may affect aquatic communities and processes. Because production forestry is practiced on a large area in both countries, the various stressors appear multiple times during the rotation cycles and potentially affect the majority of the stream network length within most catchments. We concluded that forestry practices have traditionally not been the focus of multiple stressor studies and should be investigated further in both observational and experimental fashion. Stressors accumulate across time and space in forestry dominated landscapes, and may interact in unpredictable ways, limiting our current understanding of what forested stream networks are exposed to and how we can design and apply best management practices.

Enactment: A necessary conceptual review

ABSTRACT Enactment as a psychoanalytic concept was coined by Jacobs in 1986. Since then, it has developed within the psychoanalytic community as a concept. Originally, enactment described an aspect of countertransference expressed through subtle actions within the analytic process, as distinct from noisier performances that fall within the spectrum of “acting out”. Nowadays, the concept of enactment poses some difficulties that should not be ignored. As a theoretical concept, enactment has a complex epistemological position, and as a clinical concept it sheds light on contemporary psychoanalytic practices. This paper explores the results from an empirical and conceptual research work, focusing on the history, translations and usages of the term “enactment”, and its comparison with related terms. It will also consider the epistemological perspective regarding the current status of enactment as a scientific concept and its relationship with the psychoanalytic theoretical field.

Solvent crystallization-induced porous polyurethane/graphene composite foams for pressure sensing

Fabrication of facile and scalable flexible graphene-based composite materials offer many advantages and have promising applications as pressure sensors. However, fabricating graphene-based materials with high and wide-range pressure sensitivity in a facile and large-scale way remains a big challenge. Current study details a simple solvent crystallization method to fabricate porous polyurethane and graphene (PU/G) foams with outstanding mechanical stability that can be made into any shape and size. The microstructure and performance of the PU/G foams confirm an ultra-high sensitivity of 7.62 kPa−1 and a super wide pressure range up to 500 kPa with high stability. More importantly, as prepared composite foams can be naturally attached to detect body motions such as finger bending, walking and jumping, which renders its applications in various areas for pressure sensing. As developed PU/G pressure sensor has demonstrated sensing capabilities in monitoring body motions from subtle actions to vigorous activities.

Strain Sensor with Both a Wide Sensing Range and High Sensitivity Based on Braided Graphene Belts.

Recent years have witnessed significant development of flexible strain sensors in a variety of fields. Nevertheless, the challenge of integrating a broad sensing range (>50%) with high sensitivity [gauge factor (GF) value > 100 over the entire sensing strain] in one single flexible strain sensor still exists. Herein, we prepared a flexible strain sensor based on braided graphene belts (BGBs) and dragon skin. Such a BGB strain sensor exhibits an integration of a wide sensing range (up to 55.55%) and high sensitivity (GF value > 175.16 through the entire working range). Besides, this BGB strain sensor also demonstrates a minute monitoring limit (0.01%), low hysteresis and overshoot behaviors, and reliable cycling repeatability (>6000 cycles). The SEM microscopy observations reveal that the skew angle and intersection regions of graphene belts are mainly responsible for the desirable sensing performance. Finally, the successful detection of full-range human motions, from subtle actions to vigorously joint-related movements, reflects great potential of the BGB strain sensor in the application of wearable instruments.

Recognition of Subtle Gestures by 2-Channel sEMG Using Parameter Estimation Classifiers Based on Probability Density

The abundant movement information carried by the surface electromyography (sEMG) signal can comprehensively reflect the muscle movement patterns on the surface of the human body, and the characteristics of physiological signals involving movement information have promoted the vigorous development in the field of human-computer interaction in machine learning. This paper takes subtle gestures as the research objects, and proposes a subtle gestures recognition system that uses two-channel sEMG signal and utilizes Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) which are parameter estimation classifiers based on probability density. First, combining the raw sEMG signal and the envelope sEMG signal, the number of channels for sensors and corresponding position of the muscle groups are preferably selected. Then, we propose to use the Pearson correlation coefficient to optimize the four types of features extracted in the time domain, frequency domain, time-frequency domain and AR model parameters, which effectively ensures the uniqueness of each gesture and eliminates individual differences. Finally, this paper compares four classifiers of QDA, LDA, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), and explores the pattern recognition classifier suitable for subtle actions. The experiment collects 9 kinds of subtle gestures from 8 subjects. The results show that the system can use only two channels of sEMG signal to recognize 9 kinds of subtle gestures which include multi-finger clicking or pinching or holding objects, and the most suitable classifiers QDA and LDA have the average recognition rates of 95.79% and 95.01%, respectively.

Human Action Recognition Using Improved Sparse Gaussian Process Latent Variable Model and Hidden Conditional Random Filed

Owing to the fragile to external environment, current human activity recognition is still challenging in the area of computer vision. In particular, the recognition of subtle actions is a difficult task to distinguish from each other. In this paper, we proposed the methods for human action recognition based on improved sparse Gaussian process latent variable model (GPLVM) and hidden conditional random field (HCRF). We first fused the skeletal information and the motion characteristics of human body to extract human action features from a set of action sequences. And then, we proposed an improved sparse GPLVM algorithm for feature dimensionality reduction to satisfy the visualization and computation complexity for human actions recognition. Through sparse approximation with information vector machine and the dynamic process of human actions, the improved sparse GPLVM allows the action features to be represented by the low dimensional trajectory in manifold space. Furthermore, HCRF was applied to recognize the characteristics of human actions derived from manifold spaces and finally to classify human actions. The proposed methods are tested and evaluated on public action database. The experimental results show that our methods can achieve better performance of feature dimensionality reduction and visualization and obtain the average recognition accuracy of 93.68%.

Three-dimensional topographies of water surface dimples formed by superhydrophobic water strider legs

A water strider has a remarkable capability to stand and walk freely on water. Supporting forces of a water strider and a bionic robot have been calculated from the side view of pressed depth of legs to reconstruct the water surface dimples. However, in situ measurements of the multiple leg forces and significantly small leg/water contact dimples have not been realized yet. In this study, a shadow method was proposed to reconstruct the in situ three-dimensional topographies of leg/water contact dimples and their corresponding supporting forces. Results indicated that the supporting forces were affected by the depth, width, and length of the dimple, and that the maximum dimple depth was not proportional to the supporting forces. The shadow method also has advantages in disclosing tiny supporting force of legs in their subtle actions. These results are helpful for understanding the locomotion principles of water-walking insects and the design of biomimetic aquatic devices.

CYBERBULLYING: A CLINICIAN’S PERSPECTIVE

Traditionally bullying is referred to as repeated aggressive behavior, including physical acts such as hitting, shoving, verbal abuse, such as name-calling, as well as more subtle actions, such as social exclusion and rumorspreading. 1,2 In recent years, social media has been a dominant platform for communication amongst teens in our communities. By 2010, the proportion of American teens using social media had risen to 73% from 65% in 2008.3 A report from 2012 indicated that at least 77% of American teens (12–17 years old) had cell phones, and 23% owned smart phones.4