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

作者： Islam, Md Saiful Rahman, Md Mahbubur Bin Morshed, Mehrab Lin, David J Li, Yunzhi Zhou, Hao Mendes, Wendy Berry Kuang, Jilong Department of Computer Science University of Rochester RochesterNY United States Digital Health Team Samsung Research America Mountain ViewCA United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States The Pennsylvania State University State collegePA United States Psychology Yale University New HavenCT United States

ISBN: (纸本)9798400713941

This paper examines the potential of commercial earbuds for detecting physiological biomarkers like heart rate (HR) and heart rate variability (HRV) for stress assessment. Using accelerometer (IMU) and photoplethysmography (PPG) data from earbuds, we compared these estimates with reference electrocardiogram (ECG) data from 81 healthy participants. We explored using low-power accelerometer sensors for capturing ballistocardiography (BCG) signals. However, BCG signal quality can vary due to individual differences and body motion. Therefore, BCG data quality assessment is critical before extracting any meaningful biomarkers. To address this, we introduced the ECG-gated BCG heatmap, a new method for assessing BCG signal quality. We trained a Random Forest model to identify usable signals, achieving 82% test accuracy. Filtering out unusable signals improved HR/HRV estimation accuracy to levels comparable to PPG-based estimates. Our findings demonstrate the feasibility of accurate physiological monitoring with earbuds, advancing the development of user-friendly wearable health technologies for stress management. © 2025 Copyright held by the owner/author(s).

关键词： Heart

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[DC] Context-Aware Inference and Adaptation in Augmented Reality

[DC] Context-Aware Inference and Adaptation in Augmented Rea...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Shakiba Davari Center for Human-Computer Interaction and Department of Computer Science Virginia TechUSA

ISBN: (纸本)9781665484039

Augmented Reality(AR) offers the potential for easy and efficient information access, reinforcing the wide belief that AR Glasses are the next-generation of personal computing devices. However, to realize this all-day AR vision, the AR interface must be able to address the challenges that constant and pervasive presence of virtual content can cause for the user. The optimal interface, that is the most efficient yet least intrusive, in one context may be the worst interface for another context. Throughout the day, as the user switches context, an optimal all-day interface must adapts its virtual content display and interactions as well. This work aims to propose a research agenda to design and validate different adaptation techniques and context-aware AR interfaces and introduce a framework for the design of such intelligent interfaces.

关键词： human computer interaction Three-dimensional displays Conferences Glass Next generation networking Augmented reality

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Gaze Behaviour in Adolescents with Obsessive-Compulsive Disorder During Exposure Within Cognitive-Behavioural Therapy 17th

Gaze Behaviour in Adolescents with Obsessive-Compulsive Di...

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17th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2023

作者： Thierfelder, Annika Severitt, Björn Klein, Carolin S. Alt, Annika K. Hollmann, Karsten Bulling, Andreas Ilg, Winfried Hertie Institute for Clinical Brain Research Section for Computational Sensomotorics University Hospital Tübingen Tübingen Germany Department of Computer Science Human-Computer Interaction University of Tübingen Tübingen Germany Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Tübingen Tübingen Germany Institute for Visualisation and Interactive Systems University Stuttgart Stuttgart Germany

ISBN: (纸本)9783031597169

Digital health interventions that involve monitoring patient behaviour increasingly benefit from improvements in sensor technology. Eye tracking in particular can provide useful information for psychotherapy but an effective method to extract this information is currently missing. We propose a method to analyse natural gaze behaviour during exposure exercises for obsessive-compulsive disorder (OCD). At the core of our method is a neural network to detect fixations based on gaze patch similarities. Detected fixations are clustered into exposure-relevant, therapist, and other locations and corresponding eye movement metrics are correlated with subjective stress reported during exposure. We evaluate our method on gaze and stress data recorded during video-based psychotherapy of four adolescents with OCD. We found that fixation duration onto exposure-relevant locations consistently increases with the perceived stress level as opposed to fixations onto other locations. Fixation behaviour towards the therapist varied largely between patients. Taken together, our results not only demonstrate the effectiveness of our method for analysing natural gaze behaviour during exposure sessions. The fixation analysis shows that patients allocate more attention towards exposure-related objects under higher stress levels, suggesting higher mental load. As such, providing feedback on fixation behaviour holds significant promise to support therapists in monitoring intensity of exposure exercises. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Location

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What Lies Beneath? Exploring the Impact of Underlying AI Model Updates in AI-Infused Systems

arXiv

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arXiv 2023年

作者： Mohanty, Vikram Lim, Jude Luther, Kurt Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Independent Researcher ArlingtonVA United States Department of Computer Science Center for Human-Computer Interaction Virginia Tech AlexandriaVA United States

AI models are constantly evolving, with new versions released frequently. human-AI interaction guidelines encourage notifying users about changes in model capabilities, ideally supported by thorough benchmarking. However, as AI systems integrate into domain-specific workflows, exhaustive benchmarking can become impractical, often resulting in silent or minimally communicated updates. This raises critical questions: Can users notice these updates? What cues do they rely on to distinguish between models? How do such changes affect their behavior and task performance? We address these questions through two studies in the context of facial recognition for historical photo identification: an online experiment examining users’ ability to detect model updates, followed by a diary study exploring perceptions in a real-world deployment. Our findings highlight challenges in noticing AI model updates, their impact on downstream user behavior and performance, and how they lead users to develop divergent folk theories. Drawing on these insights, we discuss strategies for effectively communicating model updates in AI-infused systems. © 2023, CC BY.

关键词： Face recognition

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TouchPose: Hand Pose Prediction, Depth Estimation, and Touch Classification from Capacitive Images 21

TouchPose: Hand Pose Prediction, Depth Estimation, and Touch...

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34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021

作者： Ahuja, Karan Streli, Paul Holz, Christian Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science ETH Zürich Switzerland

ISBN: (纸本)9781450386357

Today's touchscreen devices commonly detect the coordinates of user input through capacitive sensing. Yet, these coordinates are the mere 2D manifestations of the more complex 3D configuration of the whole hand - a sensation that touchscreen devices so far remain oblivious to. In this work, we introduce the problem of reconstructing a 3D hand skeleton from capacitive images, which encode the sparse observations captured by touch sensors. These low-resolution images represent intensity mappings that are proportional to the distance to the user's fingers and hands. We present the first dataset of capacitive images with corresponding depth maps and 3D hand pose coordinates, comprising 65,374 aligned records from 10 participants. We introduce our supervised method TouchPose, which learns a 3D hand model and a corresponding depth map using a cross-modal trained embedding from capacitive images in our dataset. We quantitatively evaluate TouchPose's accuracy in touch classification, depth estimation, and 3D joint reconstruction, showing that our model generalizes to hand poses it has never seen during training and can infer joints that lie outside the touch sensor's volume. Enabled by TouchPose, we demonstrate a series of interactive apps and novel interactions on multitouch devices. These applications show TouchPose's versatile capability to serve as a general-purpose model, operating independent of use-case, and establishing 3D hand pose as an integral part of the input dictionary for application designers and developers. We also release our dataset, code, and model to enable future work in this domain. © 2021 Owner/Author.

关键词： Tactile sensors

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Developable metamaterials: Mass-fabricable metamaterials by laser-cutting elastic structures 21

Developable metamaterials: Mass-fabricable metamaterials by ...

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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Signer, Madlaina Signer, Alexandra Sorkine-Hornung, Olga Department of Computer Science Eth Zurich Switzerland Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9781450380966

We propose a novel design of engineered, structured materials that leverages fast fabrication technologies, pushing them towards mass-fabrication. Specifically, our metamaterial is designed to be laser cut, to approximate the volumetric shape and allow for locally varying compliance. Traditional mechanical metamaterials consist of intricate cells arranged on a 3-dimensional grid, limiting them to 3D printing-which is slow. Our metamaterial is designed for laser cutting, which is drastically faster. Our structures are best described as ruffled strips of thin sheet material, such as paper, plastics, metals, etc. Users can interactively define the ruffles' anisotropic stiffness directions and local density. Our computational design tool assists users by automatically optimizing the ruffle to fill the shape's volume, and exporting the flat ruffle design ready for cutting. We demonstrate how such ruffled metamaterials can be utilized for, e.g., custom toys with locally varying compliance, custom packaging material, or lightweight formwork for architectural shells. © 2021 ACM.

关键词： Fabrication

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Relectrode: A reconfigurable electrode for multi-purpose sensing based on microfluidics 21

Relectrode: A reconfigurable electrode for multi-purpose sen...

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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Sun, Wei Chen, Yanjun Zhan, Simon Han, Teng State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences Beijing China

ISBN: (纸本)9781450380966

In this paper, we propose a reconfgurable electrode, RElectrode, using a microfuidic technique that can change the geometry and material properties of the electrode to satisfy the needs for sensing a variety of diferent types of user input through touch/touchless gestures, pressure, temperature, and distinguish between diferent types of objects or liquids. Unlike the existing approaches, which depend on the specifc-shaped electrode for particular sensing (e.g., coil for inductive sensing), RElectrode enables capacity, inductance, resistance/pressure, temperature, pH sensings all in a single package. We demonstrate the design and fabrication of the microfuidic structure of our RElectrode, evaluate its sensing performance through several studies, and provide some unique applications. RElectrode demonstrates technical feasibility and application values of integrating physical and biochemical properties of microfuidics into novel sensing interfaces. © 2021 ACM.

关键词： Electrodes

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A DV-Hop dynamic weight positioning model with genetic algorithm

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International Journal of Wireless and Mobile Computing 2021年第2期20卷 139-145页

作者： Wang, Penghong Fan, Tian Cai, Xingjuan Li, Wuchao School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan Shanxi003024 China Research Centre of Intelligent Interface and Human Computer Interaction Department of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang150006 China Jiaxing Vocational Technical College Jiaxing Zhejiang China

Wireless Sensor Networks (WSNs), as an important part of the Internet of Things (IoT), are widely deployed to the location estimation in WSNs. However, at present, the study of DV-Hop algorithm is still limited to the improvement of the sensor nodes in the ordinary region. In this paper, aiming at the complex areas under different application backgrounds, based on the analysis of the localisation principle of the traditional DV-Hop model based on optimisation algorithm, a dynamic weight positioning model based on genetic algorithm (DWMGA-DV-Hop) is proposed. The algorithm performs simulation experiments on three different types of test by adopting dynamic weight calculation model. And experimental results demonstrate that, under different test sets, compared with the standard DV-Hop algorithm, TW-PSODV-Hop and OCS-DV-Hop algorithm, this algorithm has the highest positioning accuracy and stability. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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In Vivo Bioelectronic Nose Based on a Bioengineered Rat for the Explosive Odors with Benzene Ring Detection and Identification 24

In Vivo Bioelectronic Nose Based on a Bioengineered Rat for ...

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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Ma, Jing Wang, Yimeng Yang, Jun Sun, Biao Yang, Qing Zheng, Chenguang Academy of Medical Engineering and Translational Medicine Medical College Tianjin University Tianjin China College of Precision Instruments & Optoelectronics Engineering Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798400718274

Accidents caused by explosives occur frequently, which seriously threaten people's lives and health, so it is urgent to develop new technologies for sensitively and rapidly detecting trace explosives. In this study, a bioengineered rat with the olfactory receptor (OR3) overexpression in the olfactory epithelium (OE) was successfully developed, and the unique features of three representative explosives (NT, DNT, and TNT) were conducted by recording electrophysiological-responsive signals from the olfactory bulb (OB) with implanted multichannel microelectrodes during awake-state-independent sniffing behavior induced by explosives and non-explosives. These three explosives were regarded as goal odors, while citral and air were no-goal odors. The analysis of local field potential (LFP) power changes revealed that, goal, and no-goals could be distinguished in both the vector group and the OR3 group at concentrations as low as 10-12 mol/L, and the classification performance of the OR3 group was superior to that of the vector group overall. Our findings suggest that OR3 bioengineered rats can reliably and accurately identify trace explosives, which provides a novel approach for detecting and identifying explosives with promising applications in odor discrimination in complex environments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electronic nose

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans 10

Implementation of a Hip Fracture Classification Network Base...

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10th International Conference on computer and Communications, ICCC 2024

作者： Zhao, Kui Yang, Meng Zhang, Ti Hu, Die Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China University of Chinese Academy of Science Shenyang China Peking University Third Hospital Department of Orthopedics Beijing China

Hip fractures is the most common hip clinical trauma. The majority of elderly patients experience a decrease in bone density due to osteoporosis, making them highly prone to various types of hip fractures. In this context, accurate diagnosis and classification of hip fractures are crucial for guiding clinical decision-making and ensuring the best possible patient outcomes. The BV C- N et proposed in this study is a two-stage model for the identification of hip fracture types, which is based on the integration of the V-Net and ConvNeXt architectures. First, deep learning segmentation model is trained to generate candidate fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to ascertain the type of fracture. The results show that the two-stage deep learning model proposed in this study enhances the accuracy of hip fracture type recognition and reduces the time required for manual classification, which can better assist doctors in fracture region recognition. © 2024 IEEE.

关键词： Classification ConvNeXt Hip Fracture Segmentation V-Net

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