Human Action Research Articles

Exploring health inequities through the actor‐network theory lens

AbstractSocial theory plays an important role in the nursing discipline and nursing inquiry as it helps conceptually embed nursing in the larger picture of the social world. For example, a broad category of critical theory provides a unique lens for uncovering social conditions of inequity and oppression. Among the sociological theories, actor‐network theory (ANT) is an approach to research and analysis that has recently gained interest among nurse philosophers and researchers. Studies guided by ANT seek to understand phenomena of interest as constituted within the relationships between human and nonhuman actors to understand how care practices are co‐created/enacted and how they can be made more humane. In this paper, we describe the benefits of ANT for examining healthcare access for incarcerated individuals with life‐limiting illnesses accessing palliative care and for people using illicit drugs. We argue that attention to the materiality of care practices can contribute to efforts of advancing health equity for these groups.

Simple esteem and the method of commonplaces in Pufendorf

ABSTRACT In his discussion of simple esteem—one of the moral entities meant to regulate human actions—Pufendorf invokes a juridical commonplace: the rule that, before evidence to the contrary, we should presume others to be good. This argumentative strategy is an illuminating example for understanding his method of commonplaces. The present paper has three goals: (1) to analyze how Pufendorf adopted from legal humanism the view that presumptions should be based on considerations of what comes about most easily in nature; (2) to show that Pufendorf’s inclusion of ethical aspects in the juridical presumption of goodness is an application of this view of forming presumptions; (3) to argue that Pufendorf did not regard juridical and philosophical commonplaces as expressions of a particular stage of intellectual history but rather as the result of reflection about human needs and abilities.

Strategic naturalizing in the Anthropocene: Managing cells, bodies and ecosystems

Discussions on the Anthropocene have called for increased attention to how the effects of human actions on the planet are accounted for. While much of this debate remains at a theoretical level, more studies on situated Anthropocene realities have been called for. Contributing to the latter, this article explores how experimental and clinical interventions are being accounted for in life science laboratories. Drawing on three research cases, genetically modified mosquitoes, laboratory-grown stem cell lines and assisted reproduction in the IVF clinic, the article traces how ‘strategic naturalizing’ is conducted to make sense of and justify human interventions on cells, bodies and ecosystems. Strategic naturalizing in laboratory work is situated, fluid, and also implicates the presence of the social scientist. Although naturalizing biotechnologies is not a new phenomenon, according to our observations scientists increasingly draw on notions of a planet profoundly transformed by human intervention as they conceptualize their own accountability. Consequently, we propose that strategic naturalizing is emerging as an elemental logic in the patchy local enactments of the Anthropocene and its concerns. In the context of experimental and clinical laboratory work, it is a key element in enacting good science and orienting the work towards making biotechnology acceptable in wider society. In studying this, the social scientist also is implicated in situated local enactments of the Anthropocene.

MAFormer: A cross-channel spatio-temporal feature aggregation method for human action recognition

Human action recognition has been widely used in fields such as human–computer interaction and virtual reality. Despite significant progress, existing approaches still struggle with effectively integrating hierarchical information and processing data beyond a certain frame count. To address these challenges, we introduce the Multi-AxisFormer (MAFormer) model, which is organized in terms of spatial, temporal, and channel dimensions of the action sequence, thereby enhancing the model’s understanding of correlations and intricate structures among and within features. Drawing on the Transformer architecture, we propose the Cross-channel Spatio-temporal Aggregation (CSA) structure for more refined feature extraction and the Multi-Axis Attention (MAA) module for more comprehensive feature aggregation. Moreover, the integration of Rotary Position Embedding (RoPE) boosts the model’s extrapolation and generalization abilities. MAFormer surpasses the known state-of-the-art on multiple skeleton-based action recognition benchmarks with the accuracy of 93.2% on NTU RGB+D 60 cross-subject split, 89.9% on NTU RGB+D 120 cross-subject split, and 97.2% on N-UCLA, offering a novel paradigm for hierarchical modeling in human action recognition.

What Can We Do About the Climate Crisis?

Understanding how our planet’s climate is changing because of human actions is super important if we want to keep living here. A changing climate means hotter weather, droughts, wildfires, heavy rains, storms, and floods. To fix things, we need to figure out which human activities are making the climate act this way and how we can change those activities. Everyone has a part to play—politicians, companies, engineers, and citizens. Small things like changing how we travel or use energy can help a lot. When the younger people who will inherit the planet come together and demand action on climate change, they can have a big impact. It helps when young people combine these demands with personal action, to show others that they, and lots of other people, care about this issue: by eating less red meat, using transportation wisely, pushing for changes in how businesses operate, and voting for leaders who care about the environment.

Guess what? Only correct choices forge immediate stimulus-response bindings in guessing scenarios.

A central mechanism of human action control is the prompt binding between actions and the stimuli provoking them. Perceiving the same stimuli again retrieves any bound responses, facilitating their execution. An open question is whether such binding and retrieval only emerges when stimulus-response rules are known upon taking action or also when agents are forced to guess and receive feedback about whether they were successful or not afterward. In two experiments, we tested the hypothesis that knowing rules before responding would boost binding between stimuli and responses during action-taking relative to guessing situations. Second, we assessed whether the content of the feedback matters for binding in that agents might use feedback to build correct stimulus-response bindings even for wrong guesses. We used a sequential prime-probe design to induce stimulus-response binding for prime responses that were either rule-based or guesses, and to measure retrieval of these bindings in response times and errors in the probe. Results indicate that binding and retrieval emerge for successful but not for wrong guesses. Binding effects for correct guesses were consistently small in effect size, suggesting that pre-established stimulus-response bindings from instructed rules might indeed boost binding when taking action.

A survey of video-based human action recognition in team sports

Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex actions involving multiple people interacting with objects. Actions in team sports exhibit a different nature compared to other sports, since they tend to occur at a faster pace and involve more human-human interactions. As a result, research has typically not focused on the challenges of HAR in team sports. This paper comprehensively summarises HAR-related research and applications with specific focus on team sports such as football (soccer), basketball and Australian rules football. Key datasets used for HAR-related team sports research are explored. Finally, common challenges and future work are discussed, and possible research directions identified.

Divine teleology in Spinoza's thought: an underexplored side of Spinoza’s philosophical journey

ABSTRACT This paper shows that Spinoza went through a drastic change in his view on divine teleology, and that this change is worth paying attention to. In the Short Treatise (KV), Spinoza endorsed a version of divine teleology. As is widely recognized, however, he explicitly rejects divine teleology in the Ethics. I argue that this marks a significant change in his view. To illustrate the significance, I argue that Spinoza consistently maintains the following two premises in both the KV and the Ethics: (1) human beings act on account of some end; (2) human actions are actions of God. While the endorsement of divine teleology is consistent with these two, the rejection of divine teleology results in a trilemma. The trilemma, to be sure, can be resolved by introducing referential opacity to divine action contexts. I argue, however, that it is worth asking why Spinoza revised his view in this way.

A novel hybrid deep learning approach with GWO–WOA optimization technique for human activity recognition

The effectiveness of Human Activity Recognition (HAR) models can be largely attributed to the components derived from domain expertise. The classification system swiftly and effectively categorizes human physical activity by utilizing a comprehensive collection of variables. To construct HAR models and categorize different activities, deep learning algorithms have recently seen increased application in academic research for autonomously extracting features from raw sensory data. The primary goal of this study is to develop a strong and accurate activity classification model by first extracting features from an input dataset and then using deep learning models. In this research, we look at how well the model does in the field of HAR. Analyzing time-series data from smartphones and wearable sensors, algorithms based on deep learning (DL) have been used to forecast various human actions. Applying DL-based methods to time-series data presents a variety of challenges, notwithstanding their usage for activity identification. If the suggested approach is put into practice, the concerns mentioned before may be alleviated. To construct HAR classification techniques using data collected from wearable sensors, this study presents two Hybrid Learning Algorithms (HLA). This research employs Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) methods to teach the model sequential and spatial patterns. The process of selecting characteristics has been enhanced through the use of optimization techniques. Together, the Whale Optimization Algorithm and the Grey Wolf Optimizer form a Hybrid Optimization approach, which is introduced in this study. By combining and capitalizing the benefits of both approaches, the hybrid technique enhances the optimization process as a whole. This research may also assist individuals who have undergone amputations or experienced strokes in selecting an appropriate model and developing a personalized gait reference trajectory.

Rethinking the Social Determination of Food in Chile Through Practices and Interactions of Actors in Food Environments: Nonexperimental, Cross-Sectional Study.

Food environments are crucial for promoting healthy and sustainable eating and preventing obesity. However, existing food environment frameworks assume an already installed causality and do not explain how associations in food environments are established or articulated, especially from an integrative and transdisciplinary approach. This research attempts to bridge these gaps through the use of Actor-Network Theory, which traces the relationship network between human (and nonhuman) actors in order to describe how these interact and what agencies (direct or remote) are involved. This study aims to explain the practices and interactions of actors in food environments in order to approach the problem of unhealthy eating with a transdisciplinary approach. This is a nonexperimental, cross-sectional study. Due to the complexity of the study phenomena, a mixed methods approach with 4 consecutive phases will be developed in Chile. Phase 1 involves a systematic literature review of food environment evidence since 2015, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol; phase 2 involves the application of a shortened version of the Nutrition Environment Measure Scale-Perceptions adapted to Chile (NEMS-P-Ch) in 2 neighborhoods with different socioeconomic levels; in phase 3, six focus groups in each neighborhood will be conducted to address social determinants such as gender, employment status, and migration; and in phase 4, participant observation and in-depth interviews will be used to analyze the direct and empirical exploration of the actors in their daily interaction with food environments. The triangulation and complementarity of the data will allow us to create a practical model about the practices and interactions of actors in their food environments, which reflects the complexity and transdisciplinary nature of the study. We have advanced in phases 1-3 of the study. In phase 1, a total of 109 manuscripts are being revised for data extraction. In phase 2, we applied the NEMS-P-Ch to 785 people, 49.4% (388/785) of whom belong to a low socioeconomic neighborhood. Participants from phase 2 are being contacted to participate in the focus groups (phase 3). By the end of July, we have conducted 6 focus groups with 5-11 participants. This study will provide a comprehensive understanding of how individuals interact with their food environments, offering deep insights into the factors influencing their food-related decisions. In addition, the study aims to develop a model that more accurately reflects reality by examining not only the food environments themselves but also the interactions among various stakeholders within these environments and their daily practices. The findings of this study will offer evidence-based insights to inform public policies tailored to the specific territories and communities under investigation or those with similar characteristics. DERR1-10.2196/62765.

Materialities of progressive curriculum reform: a case study of one kindergarten classroom

ABSTRACT Objective This study reviews the status of materials in curriculum and reform, as reflected in a network case study of curriculum in one kindergarten classroom in the United States. This paper focuses on the events leading up to and building upon one inquiry unit that became a focal point in promoting progressive curriculum reform at the school. Method Methods from Actor Network Theory are employed to attend to a wide cast of human and nonhuman actors, trace their mobilities, and map them into curricular networks. Data, collected over 9 months of one school year, included field notes, audio recordings of conversations, and images of classroom objects and arrangements. Results Three material assemblages—a closet, a bulletin board, and a curriculum timeline—are viewed as agents assisting in stabilizing the inquiry unit and sustaining further student-centred inquiries amidst competing curricular reforms and policies. Findings demonstrate how teachers may mobilize such assemblages to accomplish multiple objectives, as is necessary when navigating tensions from multiple parties or reform mandates. Conclusion This study contributes to scholarship conceptualizing the status of curriculum materials, with particular attention to their functionality at the nexus of both the micro-interactions of curriculum enactment and the macro-politics of educational reform.

A Single Dose of Phosphoinositide-3-kinase Inhibitor Alpelisib Induces Insulin Resistance in Healthy Adults: A Randomized Feasibility Study.

Our objective is to test a single dose of the phosphoinositide-3-kinase (PI3K) inhibitor alpelisib as a tool for acute modeling of insulin resistance in healthy volunteers. This single-center, double-blind, phase 1 clinical trial randomized healthy adults to take a single oral dose of alpelisib 300 mg (n = 5) or placebo (n = 6) at bedtime, followed by measurement of glucose, insulin, and C-peptide levels after an overnight fast and during a 3-hour, 75-g oral glucose tolerance test. Fasting plasma glucose trended higher with alpelisib (mean ± S.D.: 93 ± 11 mg/dL) versus placebo (84 ± 5 mg/dL), while mean fasting serum insulin increased nearly fivefold (23 ± 12 µU/mL vs. 5 ± 3 µU/mL, respectively) and Homeostasis Model Assessment of Insulin Resistance scored 5.4 ± 3.1 for alpelisib and 1.1 ± 0.6 for placebo. During OGTT, incremental area under the curve (AUC) for insulin was over fourfold greater with alpelisib (22 ± 15 mU/mL x min) than placebo (5 ± 2 mU/mL x min); glucose AUC trended higher with alpelisib. Single-dose alpelisib was well tolerated and produced metabolic alterations consistent with acute induction of IR, validating its use for mechanistic study of insulin action in humans. (ClinicalTrials.gov registration: NCT05733455).

LG-AKD: Application of a lightweight GCN model based on adversarial knowledge distillation to skeleton action recognition

Human action recognition, a pivotal topic in computer vision, is a highly complex and challenging task. It requires the analysis of not only spatial dependencies of targets but also temporal changes in these targets. In recent decades, the advancement of deep learning has led to the development of numerous action recognition methods based on deep neural networks. Given that the skeleton points of the human body can be treated as a graph structure, graph neural networks (GNNs) have emerged as an effective tool for modeling such data, garnering significant interest from researchers. This paper aims to address the issue of low test speed caused by over-complicated deep graph convolutional models. To achieve this, we compress the network structure using knowledge distillation from a teacher-student architecture, leading to a compact and lightweight student GNN. To enhance the model’s robustness and generalization capabilities, we introduce a data augmentation mechanism that generates diverse action sequences while maintaining consistent behavior labels, thereby providing a more comprehensive learning basis for the model. The proposed model integrates three distinct knowledge learning paths: teacher networks, original datasets, and derived data. The fusion of knowledge distillation and data augmentation enables lightweight student networks to outperform their teacher networks in terms of both performance and efficiency. Experimental results demonstrate the efficacy of our approach in the context of skeleton-based human action recognition, highlighting its potential to simplify state-of-the-art models while enhancing their performance.

In Pictures We Trust: Evaluating Digital Information and Disinformation with Phenomenon-based Learning in Secondary Schools

Abstract The existence of disinformation in online environments increases the risk that young people will be exposed to manipulated content. It is not surprising, then, researchers and educators are focusing on ways to build pupils’ multiliteracy competencies. In this article, we look at the role visual information plays when students assess the trustworthiness of online information and disinformation. The authors present data from Austrian classroom interventions, where eight teachers used a phenomenon-based learning (PhBL) approach to build their 107 pupils’ multiliteracy competencies. The authors undertook video-based and written classroom observations and conducted interviews (n = 44). They found that visual information plays a significant role in pupils’ decisions on whether to trust online information or not. Other factors that increase trust include human actors (e.g. teachers or other pupils) and easily accessible digital information (e.g. visuals or information provided by algorithms or ai). The phenomenon-based pedagogy approach meant that pupils were working in groups, giving a greater opportunity to engage in reflective dialogue, and being more critical about online information.

Arboreal Assemblages and Ecologies of Feeling: The Bodhi Tree in the Buddhist Reform Movement in Colonial South Asia

This article centres on the Bodhi tree, aśvattha/pippala or Ficus religiosa, in Bodh Gaya, India. It analyses the tree as an assemblage by combining concepts from the ancient Buddhist Pali literature with recent interventions in new materialism and posthumanism. The text foregrounds the role of emotions in the relational cosmologies of both early South Asian Buddhist texts and contemporary general ecology, tracing how they connect the tree-as-assemblage with other human and nonhuman actors. This allows the article to recover an emergent agency in the assemblage, informing a re-examination of the tree’s role in the Buddhist reform movement in colonial India, specifically in its connections to the Anagarika Dharmapala and the Maha Bodhi Society. Besides its contribution to the history of Buddhism in colonial modernity, the article thus develops theoretical reflections on writing history beyond anthropocentrism by mobilising the potential of South Asian vernacular philosophies to speak to contemporary issues in philosophy and the Anthropocene at large.

Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition

Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition

Learning human actions from complex manipulation tasks and their transfer to robots in the circular factory

Abstract Process automation is essential to establish an economically viable circular factory in high-wage locations. This involves using autonomous production technologies, such as robots, to disassemble, reprocess, and reassemble used products with unknown conditions into the original or a new generation of products. This is a complex and highly dynamic issue that involves a high degree of uncertainty. To adapt robots to these conditions, learning from humans is necessary. Humans are the most flexible resource in the circular factory and they can adapt their knowledge and skills to new tasks and changing conditions. This paper presents an interdisciplinary research framework for learning human action knowledge from complex manipulation tasks through human observation and demonstration. The acquired knowledge will be described in a machine-executable form and will be transferred to industrial automation execution by robots in a circular factory. There are two primary research objectives. First, we investigate the multi-modal capture of human behavior and the description of human action knowledge. Second, the reproduction and generalization of learned actions, such as disassembly and assembly actions on robots is studied.

The vision of the circular factory for the perpetual innovative product

Abstract The growing scarcity of global resources demands a transition from linear to circular production patterns. This article presents a novel vision for integrating linear and circular production processes within a flexible and autonomous production system to achieve perpetual product use. The approach aims to preserve the added value of products and to integrate the design of product generations and production systems. Within the circular factory, the following core aspects must be examined: predicting functions of products, managing uncertainty in used products and process sequences, learning human action for complex tasks, implementing changeable, autonomous production systems, and enabling knowledge modeling for the circular factory across domains. Aspired results are a design for circular factory, effective strategies for uncertainty management and autonomous systems adaptation as well as the externalization of operational knowledge. This research is part of the Collaborative Research Center (CRC) 1574, which explores these aspects in detail. For an in-depth understanding of specific components, it is referred to other publications by the CRC 1574.

Review on synergizing the Metaverse and AI-driven synthetic data: enhancing virtual realms and activity recognition in computer vision

The Metaverse’s emergence is redefining digital interaction, enabling seamless engagement in immersive virtual realms. This trend’s integration with AI and virtual reality (VR) is gaining momentum, albeit with challenges in acquiring extensive human action datasets. Real-world activities involve complex intricate behaviors, making accurate capture and annotation difficult. VR compounds this difficulty by requiring meticulous simulation of natural movements and interactions. As the Metaverse bridges the physical and digital realms, the demand for diverse human action data escalates, requiring innovative solutions to enrich AI and VR capabilities. This need is underscored by state-of-the-art models that excel but are hampered by limited real-world data. The overshadowing of synthetic data benefits further complicates the issue. This paper systematically examines both real-world and synthetic datasets for activity detection and recognition in computer vision. Introducing Metaverse-enabled advancements, we unveil SynDa’s novel streamlined pipeline using photorealistic rendering and AI pose estimation. By fusing real-life video datasets, large-scale synthetic datasets are generated to augment training and mitigate real data scarcity and costs. Our preliminary experiments reveal promising results in terms of mean average precision (mAP), where combining real data and synthetic video data generated using this pipeline to train models presents an improvement in mAP (32.35%), compared to the mAP of the same model when trained on real data (29.95%). This demonstrates the transformative synergy between Metaverse and AI-driven synthetic data augmentation.

A human digital twin for the M-Machine

Human digital twins are computational models of the human actions involved in interacting and operating technical artifacts. Such models provide a conceptual and practical tool for artificial intelligence designers when they seek to replace human work with intelligent machines. Indeed, digital twins have long served as models of technical and cyber-physical processes. Human digital twins have such models as their foundations but also include models of human actions. As a result, human digital twin models enable technology designers to model how people interact with intelligent technical artifacts. Yet, development of human digital twins is associated with certain conceptual problems. To clarify the basic idea, we constructed a human digital twin for Minsky’s M-Machine. The abstract conceptual structure of this machine and its generality allowed us to analyze the general properties of human digital twins, their design, and their use as tools in designing intelligent technologies.