Augmented Technology Research Articles

Velocity ratings and perceptual qualities of electrotactile stimulation of the foot sole are impacted by direction, stimulus interval, and cutaneous saltation.

Electrotactile stimulation is a çmethod of activating the tactile system by bypassing cutaneous mechanoreceptors and exciting the cutaneous afferent endings directly. This method is of interest for its potential in wearable tactile augmentation technologies. The generation of meaningful electrotactile sensation could benefit cases of peripheral neuropathy or prosthesis. There are limitations in our understanding of an electrotactile stimulations' capacity to represent tactile sensibilities, and its susceptibility to missense. The spatiotemporal parameters of an electrotactile sequence were varied. The present work extends the assessment of subjective evaluations of localization, velocity, and descriptive qualities. We applied electrotactile pulses at three sites on the foot sole, using three patterns across these sites: toward the heel or toes. We tested at three interstimulus intervals (100 ms, 160 ms, 220 ms). Faster sequences produced higher velocity ratings. Sequence direction across the foot sole impacted velocity ratings-with heel-to-toe sequences demonstrating a higher velocity rating than toe-to-heel sequences. During faster sequences with site repetition, cutaneous saltation is likely causing missense during localization. The spatiotemporal missense did not impact velocity ratings. This indicates that certain aspects of electrotactile sequence perception, such as velocity, are preserved through tactile illusions. These findings may be used to increase the resolution of stimulating grids.

Fundus Vascular Segmentation Based on Data Enhancement and Invariant Feature Extraction

Deep neural networks have emerged as the predominant method for medical image segmentation, owing to their robust feature learning capabilities, enabling accurate automatic segmentation of target structures within intricate medical images. Fundus images, being crucial in diagnosing ophthalmic diseases, underscore the importance of effective segmentation techniques. However, fundus vascular images pose challenges due to their high complexity and subtle individual differences, necessitating improvement in existing segmentation methodologies for enhanced disease classification accuracy. This paper introduces a fundus blood vessel segmentation model, employing data augmentation and invariant feature extraction, to systematically tackle the core challenges in medical image processing, particularly in fundus blood vessel segmentation. These challenges include limited source domain samples and inadequate model domain generalization. The model adopts a dual-dimensional strategy. Firstly, it delves into data augmentation technology to enhance the diversity and representativeness of samples within the finite source domain. This is achieved through an image enhancement module based on Fourier transform, mitigating the impact of data scarcity on model training effectiveness. Secondly, the research focuses on interdomain invariant feature extraction, aiming to extract feature representations that consistently characterize fundus blood vessel structure and pathology across different data distributions, thereby enhancing model generalization performance in unfamiliar domains. Specifically, the paper designs a fundus image enhancement module based on Fourier transform in the data augmentation dimension. In the feature extraction dimension, it proposes a normalization module based on uncertainty theory, departing from traditional normalization methods. Experimental results demonstrate the efficacy of the proposed method, showcasing superior generalization performance compared to existing techniques in retinal blood vessel and OD/OC segmentation tasks. Experience validates the model-agnostic nature of the learned strategy, indicating its potential for seamless transferability to other models, thereby offering robust support for advancing medical image segmentation research and applications.

Role of eXtended Reality use in medical imaging interpretation for pre-surgical planning and intraoperative augmentation.

eXtended Reality (XR) technology, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), is a growing field in healthcare. Each modality offers unique benefits and drawbacks for medical education, simulation, and clinical care. We review current studies to understand how XR technology uses medical imaging to enhance surgical diagnostics, planning, and performance. We also highlight current limitations and future directions. We reviewed the literature on immersive XR technologies for surgical planning and intraoperative augmentation, excluding studies on telemedicine and 2D video-based training. We cited publications highlighting XR's advantages and limitations in these categories. A review of 556 papers on XR for medical imaging in surgery yielded 155 relevant papers reviewed utilizing the aid of chatGPT. XR technology may improve procedural times, reduce errors, and enhance surgical workflows. It aids in preoperative planning, surgical navigation, and real-time data integration, improving surgeon ergonomics and enabling remote collaboration. However, adoption faces challenges such as high costs, infrastructure needs, and regulatory hurdles. Despite these, XR shows significant potential in advancing surgical care. Immersive technologies in healthcare enhance visualization and understanding of medical conditions, promising better patient outcomes and innovative treatments but face adoption challenges such as cost, technological constraints, and regulatory hurdles. Addressing these requires strategic collaborations and improvements in image quality, hardware, integration, and training.

Towards a Hierarchical User Requirement Structure for Upper Limb Assistive Robotics

This article presents the results of an interdisciplinary study encompassing data collected from 108 in-depth interviews and 15 participatory workshops aimed at exploring the requirements of a diverse range of stakeholders in relation to assistive augmentative technologies. The study involved 39 post-stroke patients with hand paresis, 39 individuals with spinal cord injuries affecting upper limb function, 38 relatives and 65 healthcare professionals. Through qualitative analysis of the accumulated data, this research identified the multifaceted needs, preferences and aspirations of the study participants. We propose a structured hierarchy for user requirements, inspired by Maslow's hierarchy of needs. At the foundation of this pyramid are the essential functional requirements, forming the bedrock for the subsequent layers. Moving hierarchically, we identified reliability and security requirements, followed by usability and user experience considerations, recognition requirements and, finally, personal growth and self-fulfilment criteria. The foundational functional requirements must be fulfilled before other aspects can take precedence, creating a systematic approach to address the diverse needs of individuals with upper limb disabilities. The pyramid of requirements identified may significantly contribute to the scholarly community and lay the foundation for the development of assistive robotics that align with the desires of individuals with upper limb disabilities.

The Impact of Transferring Embodiment and Work Efficiency Between Natural Body and Modular Body Systems

Human augmentation technology, particularly supernumerary robotic limbs, has seen rapid growth and offers promising applications. However, the cognitive aspects of supernumerary robotic limbs, such as the sense of embodiment, remain underexplored in the context of modular body systems that which provide the part of one’s own body feeling, especially when detached, and how this might impact work efficiency. This study aims to investigate the impact of experience of synchronizing Sense of Embodiment and work efficiency between the user’s innate body and a modular body system. The experiment was conducted using a modular body prototype and compared between with wearing experience (the user is more likely to perceive this robot as a part of their body) and without wearing experience (the user is more likely to perceive this robot as a separate, standalone robot). Objective evaluations included task completion time and accuracy, while the sense of embodiment questionnaire was employed for subjective evaluations. The results suggested that after having experience of wearing modular arm for a while, this experience can influence the work efficiency even if it was used without wearing it.

An automatic detection method for cervical liquid‐based cells based on improved Yolov5s network

AbstractTo address the significant challenges of high false positive and false negative rates in existing algorithms for detecting cervical fluid‐based cells, an enhanced Yolov5s network is introduced. This paper details a novel approach that dynamically adjusts the weights of channels and the spatial attention in modules, substantially improving feature extraction from small objects and boosting the detection capabilities of the network. Furthermore, Mixup data augmentation technology is incorporated to counter the issue of imbalanced data categories in the custom dataset. The Complete Intersection over Union loss function is also employed to refine coordinate localization accuracy during training. Tested on the proprietary cervical cytology dataset, the modified Yolov5s achieves a mean Average Precision of 92.1%, surpassing the previous state‐of‐the‐art by 5.6%. This enhancement substantiates the efficacy of the proposed model. Code and models are accessible at https://github.com/youyi888/yolov5_CPCA.

Cloud based adaptive learning system: virtual reality and augmented reality assisted educational pedagogy development on clinical simulation

An adaptive learning system provides support for student’s academic progress by varying learning routes and pedagogical contents to satisfy various students’ unique academic needs. The primary goal of this research work is to enhance and expand occupational efficiency through the use of technological pedagogical content knowledge (TPCK) frameworks in nursing education. The authors developed an adaptive federated cloud computing learning system in order to better prepare students for the demands of real-world professional practice. The article examined the use of virtual reality(VR) and augmented (AR) technologies in nursing education and developed an instrument that skillfully combines knowledge from the reputable literature on clinical competencies with the peculiarities and ingenuousness particular to virtual simulations. Designated for the remote sharing and utilization of combined educational resources, cloud computing is a distributed information communication technology (ICT) infrastructure built on the Internet of Things (IoT) framework. To increase the occupational efficiency among the nurses and midwives, an educational simulation applied in a way that gamifies the curriculum component for interactive educational pedagogy will be necessary. As a completely integrated part of the curriculum, simulation has proven to be beneficial for nursing students at all levels of instruction. Nursing educators have a plethora of options at their disposal to introduce simulation-based learning approaches into their curricula in the modern era. A three-dimensional (3D) interactive environment that provides users with a sense of spatial presence is created through computer technology, which is an immersive digital ecosystem of VR and AR utilized to improve teaching and learning in nursing education. In this study, the authors offered a cost- effective clinical simulation laboratory for clinical students, nurses, midwives, and practicing healthcare professionals in a manner that ensures constant learning and occupational efficiency by adopting the TPCK model in its central discussion and implementation. The TPCK model has demonstrated how to recognize the boundaries between technological components and pedagogical structures as well as how they are connected when creating facts in nursing education.

Transhuman Communication: Human Augmentation Technologies through Co-Speculation Workshops

The advancement of human-computer integration as a research field promises to introduce transhumanistic ways of communicating through the enhanced abilities of augmented humans. Our work seeks to illuminate this experiential landscape, exploring a diverse set of human augmentation technologies (HATs) for communication purposes. We investigated this topic through four co-speculation workshops focusing on physical, cognitive, sensory and emotional augmentations with 35 participants. Through a reflexive thematic analysis of the workshop data, we outlined eight HAT speculations for transhuman communication, grouped into four overarching augmentation clusters: (1) Bodily Changes in/for Communication, (2) Communication with Transferrable and Collective Beings, (3) Communication through Emotion and Memory, and (4) Communication with Augmented/Altered Perception. By serving as a foundation for discussions on transhumanism and communication in CSCW and beyond, these speculations contribute to a design space highlighting design opportunities and challenges to developing and researching near-future communication technologies.

A Data Augmentation Method and the Embedding Mechanism for Detection of Pulmonary Nodules on Small Samples

Abstract Lung Computed Tomography (CT) screening for pulmonary nodules provides an effective method for early diagnosis. The deep-learning-based computer-aided detection (CAD) system effectively identifies and precisely localizes suspicious pulmonary nodules in CT images, thereby significantly enhancing the accuracy and efficiency of CT diagnosis. In the medical field, the availability of medical data is limited, and research using small samples is of practical significance. By studying the data augmentation technology based on the generative model under the condition of small samples, and refining the model structure through the embedding mechanism, the accuracy and robustness of the deep learning model are explored. A 3D pixel-level statistical algorithm is proposed for the generation of pulmonary nodules. By combining simulated pulmonary nodules with healthy lung tissue, we can generate new samples of pulmonary nodules. The embedding mechanism is designed to enhance the comprehension of pixel meanings in pulmonary nodule samples by introducing latent variables. The results of the 3DVNET model with the augmentation method for pulmonary nodule detection under small sample conditions demonstrate that the proposed data augmentation method outperforms the method based on a generative adversarial network (GAN) framework, and the embedding mechanism for pulmonary nodules detection shows a significant improvement in accuracy. Conclusion: the proposed data augmentation method and embedding mechanism demonstrate significant potential in enhancing the accuracy and robustness of the model, thereby facilitating their application to various common imaging diagnostic tasks, and research using small samples is of practical significance.

Human-robot collaborative decision method of hexapod robot based on prior knowledge and negotiation strategy

Against the unstructured terrain environment, human drivers often rely on remote operating patterns to accomplish tasks with robots. With the gradual improvement in robot intelligence, a key problem that urgently requires attention is how to bridge cognitive and perceptual differences between human and robot in collaborative decision-making processes. Particularly effective resolution of differences in decision outcomes is crucial. Therefore, this paper utilizes the decision logic process and prior knowledge model of human drivers to form a robot's decision framework, narrowing the cognitive differences between human and robot. Besides, embedding the human-robot-environment features that affect the decision-making process as prior knowledge into the robot's decision-making system can effectively reduce perceptual gaps. Under the same decision framework, a negotiation strategy is proposed for forming a unified decision outcome between human and robot by finding an optimal concession rate. This study utilizes a hexapod robot simulated driving platform to gather experimental data and develop prior knowledge for robots. It also leverages virtual reality equipment and visual augmentation technologies to establish a remote human-robot collaborative decision-making experimental system. The experimental results conducted in complex obstacle terrain demonstrate the effectiveness of adopting the negotiation strategy for human-robot collaborative decision-making. Compared with the individual human or robot decision, the human-robot collaborative decision method significantly enhances robot stability, reduces energy consumption, and minimizes collisions.

Internet of Things and the Evolution of Human 2.0: An Analytical Study on Enhancing Human Capabilities

The Internet of Things (IoT) technology has introduced new tools for enhancing human capabilities, promising improved quality of life and professional efficiency. The concept of "Augmented Human" (AH) refers to using technology to enhance and improve natural human abilities, whether physical or cognitive, through devices and technologies that boost sensory perception, physical strength, and cognitive performance. However, the integration of IoT with human augmentation technology has expanded the scope of IoT-related challenges beyond current boundaries. So far, these potential challenges associated with IoT-enabled human augmentation have not investigated. Therefore, this article aims to introduce readers to the concept of human augmentation (AH) and to summarize the key research challenges posed by such systems, thereby encouraging readers' interest in this topic. The article explores emerging IoT applications in human augmentation, along with devices and design principles, connectivity requirements, and security aspects. Keywords: Internet of Things, human capabilities, flexible hybrid electronics.

Exploratory acoustic analysis of nonverbal affective and communicative vocalizations from non-speaking individuals with autism using cosine similarity

Nonverbal vocalizations from non-speaking individuals are challenging to interpret without context or familiarity. This study assessed acoustic similarities between 4727 affective and communicative vocalizations (e.g., frustration, delight, request) from 8 non-speaking individuals with autism using the ReCANVo dataset. We augmented the audio with noise, pitch shifts, and tempo changes, and extracted features using pre-trained Wav2Vec2 model representations, mel-spectrograms, and low-level descriptors to train a classification network (dim 24; 85/15 train/val). We calculated a mean vector (dim 24) for each class and computed their cosine similarities. Across eight individuals and seven vocalization classes, “Social” vocalizations were most globally similar (AvgCS = 0.950), suggesting acoustic features spanning affective and communicative states, while “Dysregulated” sounds were the most distinct (AvgCS = 0.886). However, the model (Acc = 0.660, MacroF1 = 0.555) was sensitive to speaker gender and age, and class imbalances, even after augmentation. Examining the participant with the most samples (P01; n = 1687; Acc = 0.676; MacroF1 = 0.695), “Request” was most similar to “Social” (CS = 0.978), suggesting shared underlying acoustics. “Delightedi” and “Frustrated” were the least similar (CS = 0.715), supporting the hypothesis that affective vocalizations are discernable via acoustical properties. Increased sample sizes and better real-world labeling techniques will enhance model accuracy, enabling interactive augmentative technologies for this population.

Detection Method of Stator Coating Quality of Flat Wire Motor Based on Improved YOLOv8s.

The stator of a flat wire motor is the core component of new energy vehicles. However, detecting quality defects in the coating process in real-time is a challenge. Moreover, the number of defects is large, and the pixels of a single defect are very few, which make it difficult to distinguish the defect features and make accurate detection more difficult. To solve this problem, this article proposes the YOLOv8s-DFJA network. The network is based on YOLOv8s, which uses DSFI-HEAD to replace the original detection head, realizing task alignment. It enhances joint features between the classification task and localization task and improves the ability of network detection. The LEFG module replaces the C2f module in the backbone of the YOLOv8s network that reduces the redundant parameters brought by the traditional BottleNeck structure. It also enhances the feature extraction and gradient flow ability to achieve the lightweight of the network. For this research, we produced our own dataset of stator coating quality regarding flat wire motors. Data augmentation technology (Gaussian noise, adjusting brightness, etc.) enriches the dataset, to a certain extent, which improves the robustness and generalization ability of YOLOv8s-DFJA. The experimental results show that in the performance of YOLOv8s-DFJA compared with YOLOv8s, the mAP@.5 index increased by 6.4%, the precision index increased by 1.1%, the recall index increased by 8.1%, the FPS index increased by 9.8FPS/s, and the parameters decreased by 3 Mb. Therefore, YOLOv8s-DFJA can be better realize the fast and accurate detection of the stator coating quality of flat wire motors.

The Human-Technology Continuum.

Managers in health care today face an array of digital technologies that assist or augment certain human tasks. But these technologies are often fraught and present challenges to managers, whose competencies must evolve to keep pace with technological advancements. Drawing on theory about technology, work, and organizations, we present a human-technology continuum to facilitate this discussion for managers. Furthermore, we illustrate how managerial competencies are linked to the entire human-technology continuum, rather than to specific technologies, using diabetes management examples. The human-technology continuum indicates that augmentative technologies are layered onto assistive ones in health care settings. This suggests that technological advancements not only enhance but alter managerial competencies. Digital technology stretches the boundaries of managers' day-to-day work in health care. Therefore, we make the following suggestions so the managers can be responsive to ongoing digital transformations: restructuring work, training the workforce, neutralizing threats, establishing ethical boundaries, and building partnerships.

DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients.

Axillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes. We present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis. The comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model. The experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process.

Negotiating Networked Learning Relationships with Augmentation Technologies

The hosts of this round table discussion, members of the Building Digital Literacy (BDL) research cluster of the Digital Life Institute (www.digitallife.org), adopt a critical disposition (NLEC, 2021a, 2021b) toward emerging augmentation technologies that sit at the core of networked learning. Augmentation technologies, such as wearable devices that extend human senses, augment creative abilities, or overcome physical limitations (Pederson & Hill, 2021), represent the engine that drives the next generation of networked learning. As emerging augmentation technologies, use of data analytics, and “smart” technologies proliferate, we see the critical need for research, presentation, and discussion of the implications for networked learning. This round table invites conversation about the role of artificial intelligence, big data, and learning analytics in networked learning.

The AR Cloud: Navigating Metaverse Augmentation Technologies for Enhanced Co-Creation of Value Within Services

In the rapidly evolving landscape of metaverse technologies, the potential for enhancing service interactions is immense. However, many of these technologies fall short in offering context-driven and customizable experiences. This paper proposes that AR Cloud (ARC), a novel external metaverse technology can bridge this gap. ARC stands out by enabling real-time interactions, fostering contextual awareness, ensuring high content flexibility, promoting social engagement, and delivering personalized experiences. We present a conceptual model that juxtaposes ARC with established metaverse augmentation technologies, such as lifelogging and augmented reality. Our discussion focuses on the transformative impact of ARC on the value co-creation process, emphasizing the enhanced well-being outcomes—both hedonic and eudemonic—that arise from ARC-powered service interactions. We conclude by charting a course for future research on ARC, touching upon its attributes, potential moderating variables, its role in sustained value co-creation, and the potential service outcomes it could support.

Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive Learning

Graph contrastive learning has demonstrated significant superiority for collaborative filtering. These methods typically use augmentation technology to generate contrastive views, and then train graph neural networks with contrastive learning as an auxiliary task. Although these methods are very effective, they do not consider using contrastive learning from the perspective of user–item interaction. As a result, they do not fully leverage the potential of contrastive learning. It is well known that contrastive learning can maximize the consistency of positive pairs and minimize the agreement of negative pairs. Collaborative filtering expects high consistency between users and the items they like and low consistency between users and the items they dislike. If we treat the items that users like as positive examples and the items they dislike as negative examples, contrastive learning can work very well with the goal of collaborative filtering. Based on the above understanding, we propose a new objective function called DCL loss, which improves graph collaborative filtering from the perspective of user–item interaction when Directly using Contrastive Learning. Extensive experiments have shown that when a model adopts DCL loss as its objective function, both its recommendation performance and training efficiency exhibit significant improvements.

Exploring the Potentials of Wearable Technologies in Managing Vestibular Hypofunction.

The vestibular system is dedicated to gaze stabilization, postural balance, and spatial orientation; this makes vestibular function crucial for our ability to interact effectively with our environment. Vestibular hypofunction (VH) progresses over time, and it presents differently in its early and advanced stages. In the initial stages of VH, the effects of VH are mitigated using vestibular rehabilitation therapy (VRT), which can be facilitated with the aid of technology. At more advanced stages of VH, novel techniques that use wearable technologies for sensory augmentation and sensory substitution have been applied to manage VH. Despite this, the potential of assistive technologies for VH management remains underexplored over the past decades. Hence, in this review article, we present the state-of-the-art technologies for facilitating early-stage VRT and for managing advanced-stage VH. Also, challenges and strategies on how these technologies can be improved to enable long-term ambulatory and home use are presented.

Impermanent identifiers: Enhanced source code comprehension and refactoring

In response to the prevailing challenges in contemporary software development, this article introduces an innovative approach to code augmentation centered around Impermanent Identifiers. The primary goal is to enhance the software development experience by introducing dynamic identifiers that adapt to changing contexts, facilitating more efficient interactions between developers and source code, ultimately advancing comprehension, maintenance, and collaboration in software development. Additionally, this study rigorously evaluates the adoption and acceptance of Impermanent Identifiers within the software development landscape. Through a comprehensive empirical examination, we investigate how developers perceive and integrate this approach into their daily programming practices, exploring perceived benefits, potential barriers, and factors influencing its adoption. In summary, this article charts a new course for code augmentation, proposing Impermanent Identifiers as its cornerstone while assessing their feasibility and acceptance among developers. This interdisciplinary research seeks to contribute to the continuous improvement of software development practices and the progress of code augmentation technology.