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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

Mathematical Biosciences and Engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Empirical mode decomposition

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The fNIRS-based Emotion Recognition by Spatial Transformer and WGAN Data Augmentation Towards Developing A Novel Affective BCI

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IEEE Transactions on Affective Computing 2024年第2期16卷 875-890页

作者： Si, Xiaopeng Huang, He Yu, Jiayue Ming, Dong Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Institute of Applied Psychology Tianjin300072 China Tianjin University Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin300072 China

The affective brain-computer interface (aBCI) facilitates the objective identification or regulation of human emotions. Current aBCI mainly relies on electroencephalography (EEG). However, research shows that emotions involve a large-scale distributed brain network. Compared to electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) offers a higher spatial resolution. It holds greater potential in capturing emotional spatial information, which may foster the development of new affective Brain-computer Interfaces (aBCI). We proposed a novel self-attention-based deep-learning transformer language model for fNIRS cross-subject emotion recognition, which could automatically learn the emotion's spatial attention weight information with strong interpretability. Besides, we performed data augmentation by introducing the wasserstein generative adversarial networks (WGAN). Results showed: (1) We achieved 84% three-category cross-subject emotion decoding accuracy. The spatial transformer module and WGAN improved the accuracy by 12.8% and 4.3%, respectively. (2) Compared with cutting-edge fNIRS research, we led by 10% in three-category decoding accuracy. (3) Compared with cutting-edge EEG research, we lead by 28% in arousal decoding accuracy, 10% in valence decoding accuracy, and 2% in three-category decoding accuracy. (4) Besides, our approach holds the potential to uncover the brain's spatial encoding mechanism of human emotion processing, providing a new direction for building interpretable artificial intelligence models. © 2010-2012 IEEE.

关键词： Near infrared spectroscopy

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RealityGit: Cross Reality Version Control of R&D Optical Workbench

RealityGit: Cross Reality Version Control of R&D Optical Wor...

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2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023

作者： Li, Ke Rolff, Tim Bacher, Reinhard Steinicke, Frank Deutsches Elektronen-Synchrotron Desy Germany Universität Hamburg Human-Computer Interaction Department of Informatics Germany Universität Hamburg Computer Vision Department of Informatics Germany

ISBN: (纸本)9798350328912

Multi-user collaboration at research and development (R&D) workbenches in advanced optics laboratories involves updating and documenting the status of hundreds of components. Although there are abundant mixed reality (MR) systems to support the collaboration of spatially co-located users, limited methods are available to facilitate cooperation in the spatial-temporal domain. Inspired by the version control workflow used in software development, we propose RealityGit, a novel cross-reality (CR) system design that leverages the recent advancements of neural radiance fields (NeRF). We illustrate how the NeRF model can be used to channel users with different extended reality (XR) experiences by providing an accurate immersive visual documentation of the historical states of a complex R&D workbench. We demonstrate the feasibility of such a system through an implementation where users could contribute to the version control workflows by integrating historical NeRF scans into their MR devices or providing status reviews by annotating or editing a NeRF model in virtual reality (VR). © 2023 IEEE.

关键词： Mixed reality

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Investigating the Role of Metaphors in human-AI Conversation and Engagement 24

Investigating the Role of Metaphors in Human-AI Conversation...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Maheshwary, Pragati Liu, Qiawen Mao, Rui Carnegie Mellon University Human-Computer Interaction Institute United States University of Wisconsin-Madison Department of Psychology United States Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798331530631

Metaphorical expressions are widely used in daily communication between humans to improve the understanding of both complex and abstract concepts. However, it is unclear whether the usage of metaphorical language is helpful for enhancing the conversational engagement between humans and AI. In this work, we leverage a state-of-the-art computational metaphor processing tool to gain insights from human and ChatGPT conversations. Our quantitative analysis finds that although metaphors may enhance the quality of human-AI interactions, they do not directly lead to higher levels of conversational engagement, which is measured by the duration of the conversation. However, regression analysis shows a notable relationship between user and ChatGPT metaphor usage, suggesting that ChatGPT is adept at reflecting the linguistic style of users, especially in terms of metaphorical language. Additional topic modeling and concept mapping analyses further explored the patterns of metaphorical language across various engagement levels and topics in user and ChatGPT messages. © 2024 IEEE.

关键词： Modeling languages

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Towards Context-Aware Adaptation in Extended Reality: A Design Space for XR Interfaces and an Adaptive Placement Strategy

arXiv

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arXiv 2024年

作者： Davari, Shakiba Bowman, Doug A. Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States

By converting the entire 3D space around the user into a screen, Extended Reality (XR) can ameliorate traditional displays’ space limitations and facilitate the consumption of multiple pieces of information at a time. However, if designed inappropriately, these XR interfaces can overwhelm the user and complicate information access. In this work, we explored the design dimensions that can be adapted to enable suitable presentation and interaction within an XR interface. To investigate a specific use case of context-aware adaptations within our proposed design space, we concentrated on the spatial layout of the XR content and investigated non-adaptive and adaptive placement strategies. In this paper, we (1) present a comprehensive design space for XR interfaces, (2) propose Environment-referenced, an adaptive placement strategy that uses a relevant intermediary from the environment within a Hybrid Frame of Reference (FoR) for each XR object, and (3) evaluate the effectiveness of this adaptive placement strategy and a non-adaptive Body-Fixed placement strategy in four contextual scenarios varying in terms of social setting and user mobility in the environment. The performance of these placement strategies from our within-subjects user study emphasized the importance of intermediaries’ relevance to the user’s focus. These findings underscore the importance of context-aware interfaces, indicating that the appropriate use of an adaptive content placement strategy in a context can significantly improve task efficiency, accuracy, and usability. © 2024, CC BY-NC-ND.

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Simulacrum of Stories: Examining Large Language Models as Qualitative Research Participants 25

Simulacrum of Stories: Examining Large Language Models as Qu...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Kapania, Shivani Agnew, William Eslami, Motahhare Heidari, Hoda Fox, Sarah E School of Computer Science Carnegie Mellon University PittsburghPA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400713941

The recent excitement around generative models has sparked a wave of proposals suggesting the replacement of human participation and labor in research and development-e.g., through surveys, experiments, and interviews - with synthetic research data generated by large language models (LLMs). We conducted interviews with 19 qualitative researchers to understand their perspectives on this paradigm shift. Initially skeptical, researchers were surprised to see similar narratives emerge in the LLM-generated data when using the interview probe. However, over several conversational turns, they went on to identify fundamental limitations, such as how LLMs foreclose participants' consent and agency, produce responses lacking in palpability and contextual depth, and risk delegitimizing qualitative research methods. We argue that the use of LLMs as proxies for participants enacts the surrogate effect, raising ethical and epistemological concerns that extend beyond the technical limitations of current models to the core of whether LLMs fit within qualitative ways of knowing. © 2025 Copyright held by the owner/author(s).

关键词： large language models

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Pseudo-Haptics for Weight Perception in VR: Controller vs. Bare Hand interactions with Tracking Delay and Vertical Offset

Pseudo-Haptics for Weight Perception in VR: Controller vs. B...

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Virtual Reality (ICVR), IEEE International Conference on

作者： Chiwoong Hwang Tiare Feuchtner Kaj Grønbæk Department of Computer Science Aarhus University Denmark Human-Computer Interaction Group Konstanz University Germany

ISBN: (数字)9798350364231

ISBN: (纸本)9798350364248

Training in virtual reality (VR) offers advantages such as a safe learning environment, good scalability, and cost saving. However, state-of-the-art simulations are limited in faithfully conveying real-world physical object properties such as weight without complex technologies and instrumentation of the user or environment, which may negatively impact learning. A potential solution is the application of pseudo-haptics: illusory haptic sensations induced by a mismatch between real and virtual hand positions. Researchers have proposed various pseudo-haptic techniques to alter the perception of weight in virtual environments, but their effectiveness with hand tracking compared to VR controllers is underexplored. Hence, we investigate impacts of the interaction method for two different types of pseudo-haptic feedback based on Tracking Delay and Vertical Offset. Our results indicate that pseudo-haptics can effectively facilitate the sensation of weight both with controllers and bare hands, whereby each has benefits: hand tracking can enhance body ownership, while controllers may improve perceived responsiveness of virtual environments. Also, pseudo-haptics with Tracking Delay may be preferable for repetitive lifting tasks, as Vertical Offset can be prone to higher physical demand. The current study provides insights into the design of pseudo-haptic feedback and interaction technologies for weight perception in VR.

关键词： Hands Training Manufacturing industries Solid modeling Scalability Instruments Virtual environments User experience Delays Haptic interfaces

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Exploring Effect of Level of Storytelling Richness on science Learning in Interactive and Immersive Virtual Reality 22

Exploring Effect of Level of Storytelling Richness on Scienc...

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2022 ACM International Conference on Interactive Media Experiences, IMX 2022

作者： Zhang, Lei A. Bowman, Doug Human-Centered Design Virginia Tech United States Center for Human Computer Interaction Virginia Tech Computer Science United States

ISBN: (纸本)9781450392129

Immersive and interactive storytelling in virtual reality (VR) is an emerging creative practice that has been thriving in recent years. Educational applications using immersive VR storytelling to explain complex science concepts have very promising pedagogical benefits because on the one hand, storytelling breaks down the complexity of science concepts by bridging them to people's everyday experiences and familiar cognitive models, and on the other hand, the learning process is further reinforced through rich interactivity afforded by the VR experiences. However, it is unclear how different amounts of storytelling in an interactive VR storytelling experience may affect learning outcomes due to a paucity of literature on educational VR storytelling research. This preliminary study aims to add to the literature through an exploration of variations in the designs of essential storytelling elements in educational VR storytelling experiences and their impact on the learning of complex immunology concepts. © 2022 ACM.

关键词： Virtual reality

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Towards Intelligent Augmented Reality (iAR): A Taxonomy of Context, an Architecture for iAR, and an Empirical Study

arXiv

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arXiv 2024年

作者： Davari, Shakiba Stover, Daniel Giovannelli, Alexander Ilo, Cory Bowman, Doug A. Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States

Recent advancements in Augmented Reality (AR) research have highlighted the critical role of context awareness in enhancing interface effectiveness and user experience. This underscores the need for intelligent AR (iAR) interfaces that dynamically adapt across various contexts to provide optimal experiences. In this paper, we (a) propose a comprehensive framework for contextaware inference and adaptation in iAR, (b) introduce a taxonomy that describes context through quantifiable input data, and (c) present an architecture that outlines the implementation of our proposed framework and taxonomy within iAR. Additionally, we present an empirical AR experiment to observe user behavior and record user performance, context, and user-specified adaptations to the AR interfaces within a context-switching scenario. We (d) explore the nuanced relationships between context and user adaptations in this scenario and discuss the significance of our framework in identifying these patterns. This experiment emphasizes the significance of context-awareness in iAR and provides a preliminary training dataset for this specific Scenario. © 2024, CC BY-NC-ND.

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Benefits of Machine Learning Explanations: Improved Learning in an AI-assisted Sequence Prediction Task 25

Benefits of Machine Learning Explanations: Improved Learning...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Liang, Yu Collaris, Dennis Willemsen, Martijn C. Van Wijk, Jarke J. Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Dept. Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Jheronimus Academy of Data Science Den Bosch Netherlands

ISBN: (纸本)9798400713064

Research in Explainable AI (XAI) has shown that explanations can improve users' understanding of AI models, improve user performance and potentially reduce overreliance on AI predictions. However, this is mostly evaluated by static rather than dynamic measures, and the role of XAI on learning over trials is rarely studied. In this study, we use a context-free sequence prediction task, in which 458 participants predict the next symbol in a fixed sequence (with some noise) over 80 trials. We compare performance with AI and XAI advice against no AI support, and subsequently we test for learning by taking away the AI support after 40 trials (i.e., a reversal study design). Our results show that users learn faster with XAI than with AI without explanations or no AI and are better able to recover in performance from the removal of AI. However, the benefits of XAI on learning are much smaller for more difficult tasks. This work demonstrates the benefits of repeated measures user studies and multilevel modeling to better understand learning processes in XAI. It also shows the potential of AI explanations to help users to learn and poses XAI design suggestions to support learning in human-AI collaboration. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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