Data storytelling workflows ask learners to integrate analytical, design, and narrative skills, but instructors rarely have the capacity to provide detailed feedback at each step. Computational and AI-assisted storytelling offers opportunities to support student learning, but how feedback should be structured effectively remains unclear. To address this gap, we conducted a two-phase participatory design study. Through participant observations (N=8) and interviews (N=6), the first phase explored learners and educators’ feedback needs and challenges in a data storytelling course. The second phase conducted two design workshops (N=8/10) to design and evaluate feedback strategies (frequency, seamlessness, accountability) for Story Studio: an AI-assisted narrative storytelling application. Our findings show that participants perceived on-demand and process feedback modes as effective, but automatic and outcome feedback as slightly more persuasive. We discuss implications for designing AI-augmented storytelling systems that adapt their feedback modes to the diverse needs and expectations of students.

As data storytelling—communicating insights through data—becomes increasingly essential across disciplines, the need for scalable instructional support in this area has never been greater. While the fields of information visualization and narrative design offer rich foundations, their integration into data science education remains limited. Educators are often left without adequate tools or pedagogical frameworks to teach data storytelling at scale. To address this gap, we introduce Story Studio Plus, an AI-empowered coaching tool designed to support collaborative learning among students, educators, and intelligent agents. Developed through iterative co-design with teachers, students, and domain experts, Story Studio blends principles from learning sciences, human-computer interaction, and narrative visualization. The tool provides formative feedback, scaffolded prompts, and interactive examples tailored to students' developmental stages. Uniquely, it positions AI not as a replacement for human instruction but as a collaborative partner—enhancing teacher facilitation, supporting student agency, and fostering a co-creative classroom culture. In this tutorial, participants will engage hands-on with Story Studio's latest features and explore how human educators, learners, and AI systems can co-construct knowledge through data storytelling. Participants will also contribute feedback to shape future iterations of the tool. This session offers an applied lens on how AI can be meaningfully integrated into data science education. This project is partially supported by National Science Foundation grants 2302794 and 2302795. We would like to thank software development work by Emily Jackson and other members from the UA SAIL lab and the human-centered design work led by Jennifer Posada from University of Maryland, Baltimore County.

This study introduces an AI-powered data storytelling agent designed to support data science educators by automatically curating high-quality, real-world data stories. The system streamlines the discovery of relevant instructional examples for specific teaching activities, including assignments, quizzes, classroom discussions, and case studies, by utilizing automated classification and narrative analysis. Our prototype significantly reduces instructor preparation time while improving the diversity, quality, and pedagogical alignment of curated stories. This innovation enables educators to more efficiently source, annotate, and deploy impactful data narratives tailored to their teaching and research objectives.

Data storytelling is essential in data science education but often lacks structured guidance. While students learn visualization and modeling, existing AI tools primarily generate stories automatically rather than teaching narrative construction. Few tools integrate storytelling with Jupyter Notebooks, and those that do focus on code generation rather than user-driven storytelling. StoryStudio bridges this gap by integrating with JupyterHub, allowing users to export figures and code into an interactive storytelling interface. It supports figure organization, AI-assisted insight extraction, and structured narrative generation using seven storytelling patterns. Unlike automated tools, Story Studio emphasizes active learning, helping students craft and refine their own data narratives. This poster will showcase Story Studio's role in enhancing visual literacy and data communication in data science education.

The demand for data storytelling, or communication with data, has surged significantly in recent years. Despite its growing recognition, large-scale coaching support tools for students and instructors remain lacking. The field of information visualization has explored data visualization and storytelling. Still, this knowledge has yet to be integrated with learning science in classroom settings to enhance student learning. Post-secondary data science educators require specialized tools and guidance to teach data storytelling on a larger scale. This tutorial invites those interested in the pedagogy of data storytelling to explore the initial version of a new coaching tool, Story Studio, developed by our team. The tool's design is based on our understanding of the data storytelling process and the application of relevant learning science principles, incorporating insights from experts, educators, and students. During the tutorial, participants will engage in hands-on experiences with the tool and interact with the research and development team, providing valuable feedback to support the tool's iterative improvement. This session aims to bridge the gap between data storytelling research and educational practice, equipping educators with the necessary resources to effectively teach this critical skill set. This material is based upon work supported by the National Science Foundation under Grant No. 2302794 and 2302795.

Data storytelling is the skill to communicate data effectively and efficiently. Effective data storytelling goes beyond data visualization and focuses on explanation with clear rhetorical functions. It starts with a set of data insights collected from the data science workflow and involves iterative and interactive processes of filtering those insights into story slices, from which data stories can be created through ordering, organizing and narration. Data storytelling is an integral component of a well-rounded data science education, which complements foundational skills like quantitative reasoning and programming. Despite its significance, solid understanding of the theory and practice of developing data storytelling competency is lacking. Data storytelling is often perceived as a mythical process where quantitative information magically transforms into compelling narratives. Designing scalable coaching tools for data storytelling requires leveraging multidisciplinary expertise from learning science, computer science, data science, communication science, and human-centered design. In this workshop, we will share some initial findings and reflections from our interdisciplinary team searching for effective coaching methods and tools to support coaching data storytelling at scale. We will present results from literature reviews and expert interviews which will be packaged into a set of foundational tools such as mental model, cognitive processes and schema for story construction, assessment strategy, as well as preliminary ideas of tools to support data storytelling coaching. We hope to use this workshop to build a community of researchers and practitioners in coaching data storytelling in postsecondary formal and informal learning context.

Poor signal-to-noise ratios (SNRs) and low spatial resolutions have impeded low-cost pyroelectric infrared (PIR) sensors from many intelligent applications for thermal target detection/recognition. This article presents a cognitive signal conditioning and modulation learning framework for PIR sensing with the following two innovations to solve these problems: 1) a reconfigurable signal conditioning circuit design to achieve high SNRs and 2) an optimal sensor mask design to achieve high recognition performance. By using a programmable system on chip, the PIR signal amplifier gain and filter bandwidth can be adjusted automatically according to working conditions. Based on the modeling between PIR physics and thermal images, sensor masks can be optimized through training convolution neural networks with large thermal image datasets for feature extraction of specific thermal targets. The experimental results verify the improved performance of PIR sensors in various working conditions and applications by using the developed reconfigurable circuit and application-specific masks.

With the advancement of wearable sensor technology, the use of inertial body sensors in the field of Medicine and Healthcare has increased drastically. Researchers have found that gait data is useful for identifying various motion impairments. Current research in gait analysis is incorporating the features extracted from video data, which is hard to analyze and requires expensive video capture equipment to collect data in slow motion. In this study, we utilized the ability of inertial body sensors to capture gait features of individuals with Anterior Cruciate Ligament (ACL) injury. This study also leverages the causality-based approach to find the coordination between different features of gait data. Gait data during walking, jogging, and running was collected from 131 subjects in which 109 have ACL injury. We then utilized this data to incorporate the gait assessment technique, which uses causality analysis to predict various classes of subjects based on health condition, impacted limb, and impacted limb based on gender. Performance metrics of various machine learning (ML) algorithms were compared to observe the best performing algorithm and used it to evaluate the confidence of individual subjects prediction that aids personalized classification.

Gait impairment in multiple sclerosis (MS) can result from muscle weakness, physical fatigue, lack of coordination, and other symptoms. Walking speed, as measured by a number of clinician-administered walking tests, is the primary measure of gait impairment used by clinical researchers, but inertial gait features from body-worn sensors have been proven to add clinical value. This paper seeks to understand and differentiate the physiological significance of four such features with proven value in MS to facilitate adoption by clinical researchers and incorporation in gait monitoring and analysis systems. In addition, this information can be used to select features that might be appropriate in other forms of disability. Two of the four features are computed using the dynamic time warping (DTW) algorithm: The "DTW Score" is based on the usual DTW distance, and the "Warp Score" is based on the warping length. The third feature, based on kernel density estimation (KDE), is the "KDE Peak" value. Finally, the "Causality Index" is based on the phase slope index between inertial signals from different body parts. Relationships between these measures and the aforementioned gait-related symptoms are determined by applying factor analysis to three common, clinical walking outcomes, then correlating the inertial measures as well as walking speed to each extracted factor. Statistically significant differences in correlation coefficients to the three extracted clinical factors support their distinct physiological meaning and suggest they may have complimentary roles in the analysis of MS-related walking disability.

Energy harvesting is a promising solution to the limited battery lifetimes of body sensor nodes. Self-powered sensor systems capable of quasi-perpetual operation enable the possibility of truly continuous monitoring of patients beyond the clinic. However, the discontinuous and dynamic characteristics of harvesting in real-world scenarios-and their implications for the design and operation of self-powered systems-are not yet well understood. This paper presents a mobile energy harvesting and data collection (EHDC) platform designed to provide a deeper understanding of energy harvesting dynamics. The EHDC platform monitors and records the instantaneous usable power generated by body-worn harvesters, while also collecting human activity and environmental data to provide a comprehensive real-world evaluation of two energy harvesting modalities common to body sensor networks: solar and thermoelectric. The platform was initially validated with benchtop tests and later with real-world deployments on two subjects. 7-h-long multimodal energy harvesting profiles were generated, and the environmental and behavioral data were used to expand upon previously developed Kalman filter based mathematical models for energy harvesting prediction. Results confirm the validity of the EHDC platform and harvesting models, establishing the potential for longer term monitoring of energy harvesting characteristics; thus, informing the design and operation of self-powered body sensor networks.

Nowadays, a large amount of services are deployed on the edge of the network from the cloud since processing data at the edge can reduce response time and lower bandwidth cost for applications such as healthcare in smart homes. Resource management is very important in the edge computing since it is able to increase the system efficiency and improve the quality of service. A common approach for resource management in edge computing is to assign tasks to the remote cloud or edge devices just according to several factors such as energy, bandwidth consumption, and latency. However, the approach is insufficiently efficient and falls short in meeting the requirements of handling health emergency when being applied in smart homes for healthcare. In this paper, we propose a task scheduling approach called HealthEdge that sets different processing priorities for different tasks based on the collected data on human health status and determines whether a task should run in a local device or a remote cloud in order to reduce its total processing time as much as possible. Based on a real trace from five patients, we conduct a trace-driven experiment to evaluate the performance of HealthEdge in comparison with other methods. The results show that HealthEdge can optimally assign tasks between the network edge and cloud, which can reduce the task processing time, reduce bandwidth consumption and increase local edge workstation utilization.

Nowadays, a large amount of services are deployed on the edge of the network from the cloud since processing data at the edge can reduce response time and lower bandwidth cost for applications such as healthcare in smart homes. Resource management is very important in the edge computing since it is able to increase the system efficiency and improve the quality of service. A common approach for resource management in edge computing is to assign tasks to the remote cloud or edge devices just according to several factors such as energy, bandwidth consumption, and latency. However, the approach is insufficiently efficient and falls short in meeting the requirements of handling health emergency when being applied in smart homes for healthcare. Possible health emergency needs immediate attention and different health tasks have different priorities to be processed. In this paper, we propose a task scheduling approach called HealthEdge that sets different processing priorities for different tasks based on the collected data on human health status and determines whether a task should run in a local device or a remote cloud in order to reduce its total processing time as much as possible. Based on a real trace from five patients, we conduct a trace-driven experiment to evaluate the performance of HealthEdge in comparison with other methods. The results show that HealthEdge can optimally assign tasks between the network edge and cloud, which can reduce the task processing time, reduce bandwidth consumption and increase local edge workstation utilization.

Energy harvesting offers the promise of mobile sensor systems capable of quasi-perpetual operation, but the discontinuous and dynamic characteristics of harvesting in real-world scenarios - necessary for the design and operation of self-powered systems - are not yet well understood. The paper presents a hardware platform for providing a comprehensive real-world evaluation of two energy harvesting modalities common to body sensor networks: indoor light and thermoelectric. Day-long multi-modal energy harvesting profiles were generated, which were then used to develop a mathematical model to predict real time energy harvesting values from the sampled environmental and human behavioral parameters. Experimental results demonstrate that the model is effective in calculating and predicting harvested energy in real time, and a multi-source scheme for continuous operation of self-powered sensors is demonstrated.

Step-by-step determination of gait parameters provides insight into the variability of specific gait patterns associated with frequent injuries in the lower extremities of adolescents and with geriatric syndromes of the elderly. Numerous methods have been developed for the step-by-step estimation of gait parameters, but most are expensive, obtrusive, inconvenient, and/or inaccurate. In this paper, we developed an innovative shoe, called the “Gait Tracker”, with a low power inertial measurement unit (IMU) embedded in a 3D printed sole that provides unobtrusive, continuous, and accurate step-by-step measurement of gait parameters for individual use. This shoe enables out-of-lab gait monitoring in a wide range of activities and over an extended period of time. Experimental results from controlled studies demonstrated that the Gait Tracker can recognize various gait events and provide better accuracy in stride length measurement compared to previous systems and methods.

Human motion has been reported as having great relevance to various disease, disorder, injuries and emotional state. Therefore, motion assessment using inertial body sensor networks (BSNs) is gaining popularity as an outcome measure in clinical study and neuroscience research. The efficacy of motion assessment heavily relies on the accurate temporal clustering of human motion into actions on various time scales. However, two human factors in real-world deployments of inertial BSNs make such motion assessment challenging: mounting errors (where sensor displacement and orientation do not match what is assumed by processing algorithms) and insecure mounting (where sensors are loosely worn causing them to shake during operations). In order to enhance the robustness of human actions clustering from real-world BSN data, this work leverages dynamical systems modeling with the considerations of human factors. By proposing a computational body-model framework called the piecewise linear dynamical model (PLDM), we derive a robust method to segment time series data of inertial BSNs in real-world deployment with human factors into motion primitives and actions. We test the proposed method on three different inertial BSN datasets, extract actions on different temporal scales and recognize the actions into clusters. The experimental results demonstrate the effectiveness of our approach.

Inertial body sensors have emerged in recent years as an effective tool for evaluating mobility impairment resulting from various diseases, disorders, and injuries. For example, body sensors have been used in 6-min walk (6 MW) tests for multiple sclerosis (MS) patients to identify gait features useful in the study, diagnosis, and tracking of the disease. However, most studies to date have focused on features localized to the lower or upper extremities and do not provide a holistic assessment of mobility. This paper presents a causality analysis method focused on the coordination between extremities to identify subtle whole-body mobility impairment that may aid disease diagnosis. This method was developed for and utilized in an MS pilot study with 41 subjects (28 persons with MS (PwMS) and 13 healthy controls) performing 6 MW tests. Compared with existing methods, the causality analysis provided better discrimination between healthy controls and PwMS and a deeper understanding of MS disease impact on mobility.

Gait assessment is a common method for diagnosing various diseases, disorders, and injuries, studying their impact on mobility, and evaluating the efficacy of various therapeutic interventions. The recent emergence of inertial body sensors for gait assessment addresses the limitations of visual observation and subjective clinical evaluation by providing more precise and objective measures. Inertial sensors have been included in an ongoing study at the University of Virginia Medical Center on Multiple Sclerosis (MS), a chronic autoimmune disorder of the central nervous system (CNS) that produces neurologic impairment and functional disability over time, with the goal of improving the ability to assess MS-affected gait and to distinguish between subjects with MS and those without MS. This work presents a gait assessment technique based on causal modeling to distinguish MS-affected gait and healthy gait. The approach in this work is based on the hypothesis that the strength of interaction between body parts during walking is greater in healthy controls that in MS subjects. The strength of interaction was quantified using a causality index based on the pairwise causal relationships between body parts as characterized by the Phase Slope Index (PSI) of inertial signals from pairs of body parts. In a pilot study with 41 subjects (28 MS subjects and 13 healthy controls), the approach developed in this paper provided better separability (p <; 0.0001) compared with existing methods.