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We explore how interactions inspired by drawing software can help edit text. Making an analogy between visual and text editing, we consider words as pixels, sentences as regions, and tones as colours. For instance, direct manipulations move, shorten, expand, and reorder text; tools change number, tense, and grammar; colours map to tones explored along three dimensions in a tone picker; and layers help organize and version text. This analogy also leads to new workflows, such as boolean operations on text fragments to construct more elaborated text. A study shows participants were more successful at editing text and preferred using the proposed interface over existing solutions. Broadly, our work highlights the potential of interaction analogies to rethink existing workflows, while capitalizing on familiar features.

Intra-cardiac Echocardiography (ICE) is a crucial imaging modality used in electrophysiology (EP) and structural heart disease (SHD) interventions, providing real-time, high-resolution views from within the heart. Despite its advantages, effective manipulation of the ICE catheter requires significant expertise, which can lead to inconsistent outcomes, particularly among less experienced operators. To address this challenge, we propose an AI-driven closed-loop view guidance system with human-in-the-loop feedback, designed to assist users in navigating ICE imaging without requiring specialized knowledge. Our method models the relative position and orientation vectors between arbitrary views and clinically defined ICE views in a spatial coordinate system, guiding users on how to manipulate the ICE catheter to transition from the current view to the desired view over time. Operating in a closed-loop configuration, the system continuously predicts and updates the necessary catheter manipulations, ensuring seamless integration into existing clinical workflows. The effectiveness of the proposed system is demonstrated through a simulation-based evaluation, achieving an 89% success rate with the 6532 test dataset, highlighting its potential to improve the accuracy and efficiency of ICE imaging procedures.

d/Deaf and hearing song-signers become prevalent on video-sharing platforms, but translating songs into sign language remains cumbersome and inaccessible. Our formative study revealed the challenges song-signers face, including semantic, syntactic, expressive, and rhythmic considerations in translations. We present ELMI, an accessible song-signing tool that assists in translating lyrics into sign language. ELMI enables users to edit glosses line-by-line, with real-time synced lyric highlighting and music video snippets. Users can also chat with a large language model-driven AI to discuss meaning, glossing, emoting, and timing. Through an exploratory study with 13 song-signers, we examined how ELMI facilitates their workflows and how song-signers leverage and receive an LLM-driven chat for translation. Participants successfully adopted ELMI to song-signing, with active discussions on the fly. They also reported improved confidence and independence in their translations, finding ELMI encouraging, constructive, and informative. We discuss design implications for leveraging LLMs in culturally sensitive song-signing translations., Comment: 18 pages excluding reference and appendix

As minimally verbal autistic (MVA) children communicate with parents through few words and nonverbal cues, parents often struggle to encourage their children to express subtle emotions and needs and to grasp their nuanced signals. We present AACessTalk, a tablet-based, AI-mediated communication system that facilitates meaningful exchanges between an MVA child and a parent. AACessTalk provides real-time guides to the parent to engage the child in conversation and, in turn, recommends contextual vocabulary cards to the child. Through a two-week deployment study with 11 MVA child-parent dyads, we examine how AACessTalk fosters everyday conversation practice and mutual engagement. Our findings show high engagement from all dyads, leading to increased frequency of conversation and turn-taking. AACessTalk also encouraged parents to explore their own interaction strategies and empowered the children to have more agency in communication. We discuss the implications of designing technologies for balanced communication dynamics in parent-MVA child interaction., Comment: 19 pages excluding reference and appendix

Expressing stressful experiences in words is proven to improve mental and physical health, but individuals often disengage with writing interventions as they struggle to organize their thoughts and emotions. Reflective prompts have been used to provide direction, and large language models (LLMs) have demonstrated the potential to provide tailored guidance. Current systems often limit users' flexibility to direct their reflections. We thus present ExploreSelf, an LLM-driven application designed to empower users to control their reflective journey. ExploreSelf allows users to receive adaptive support through dynamically generated questions. Through an exploratory study with 19 participants, we examine how participants explore and reflect on personal challenges using ExploreSelf. Our findings demonstrate that participants valued the balance between guided support and freedom to control their reflective journey, leading to deeper engagement and insight. Building on our findings, we discuss implications for designing LLM-driven tools that promote user empowerment through effective reflective practices., Comment: 17 pages excluding reference and appendix

In this paper, we study interior estimates for solutions to linearized Monge-Amp\`ere equations in divergence form with drift terms and the right-hand side containing the divergence of a bounded vector field. Equations of this type appear in the study of semigeostrophic equations in meteorology and the solvability of singular Abreu equations in the calculus of variations with a convexity constraint. We prove an interior Harnack inequality and H\"older estimates for solutions to equations of this type in two dimensions, and under an integrability assumption on the Hessian matrix of the Monge-Amp\`ere potential in higher dimensions. Our results extend those of Le (Analysis of Monge-Amp\`ere equations, Graduate Studies in Mathematics, vol.240, American Mathematical Society, 2024) to equations with drift terms.

With the advancement of Large-Language Models (LLMs) and Large Vision-Language Models (LVMs), agents have shown significant capabilities in various tasks, such as data analysis, gaming, or code generation. Recently, there has been a surge in research on web agents, capable of performing tasks within the web environment. However, the web poses unforeseeable scenarios, challenging the generalizability of these agents. This study investigates the disparities between human and web agents' performance in web tasks (e.g., information search) by concentrating on planning, action, and reflection aspects during task execution. We conducted a web task study with a think-aloud protocol, revealing distinct cognitive actions and operations on websites employed by humans. Comparative examination of existing agent structures and human behavior with thought processes highlighted differences in knowledge updating and ambiguity handling when performing the task. Humans demonstrated a propensity for exploring and modifying plans based on additional information and investigating reasons for failure. These findings offer insights into designing planning, reflection, and information discovery modules for web agents and designing the capturing method for implicit human knowledge in a web task.

Transesophageal echocardiography (TEE) plays a pivotal role in cardiology for diagnostic and interventional procedures. However, using it effectively requires extensive training due to the intricate nature of image acquisition and interpretation. To enhance the efficiency of novice sonographers and reduce variability in scan acquisitions, we propose a novel ultrasound (US) navigation assistance method based on contrastive learning as goal-conditioned reinforcement learning (GCRL). We augment the previous framework using a novel contrastive patient batching method (CPB) and a data-augmented contrastive loss, both of which we demonstrate are essential to ensure generalization to anatomical variations across patients. The proposed framework enables navigation to both standard diagnostic as well as intricate interventional views with a single model. Our method was developed with a large dataset of 789 patients and obtained an average error of 6.56 mm in position and 9.36 degrees in angle on a testing dataset of 140 patients, which is competitive or superior to models trained on individual views. Furthermore, we quantitatively validate our method's ability to navigate to interventional views such as the Left Atrial Appendage (LAA) view used in LAA closure. Our approach holds promise in providing valuable guidance during transesophageal ultrasound examinations, contributing to the advancement of skill acquisition for cardiac ultrasound practitioners., Comment: Accepted in MICCAI 2024; 11 pages, 3 figures

As large language models (LLMs) advance to produce human-like arguments in some contexts, the number of settings applicable for human-AI collaboration broadens. Specifically, we focus on subjective decision-making, where a decision is contextual, open to interpretation, and based on one's beliefs and values. In such cases, having multiple arguments and perspectives might be particularly useful for the decision-maker. Using subtle sexism online as an understudied application of subjective decision-making, we suggest that LLM output could effectively provide diverse argumentation to enrich subjective human decision-making. To evaluate the applicability of this case, we conducted an interview study (N=20) where participants evaluated the perceived authorship, relevance, convincingness, and trustworthiness of human and AI-generated explanation-text, generated in response to instances of subtle sexism from the internet. In this workshop paper, we focus on one troubling trend in our results related to opinions and experiences displayed in LLM argumentation. We found that participants rated explanations that contained these characteristics as more convincing and trustworthy, particularly so when those opinions and experiences aligned with their own opinions and experiences. We describe our findings, discuss the troubling role that confirmation bias plays, and bring attention to the ethical challenges surrounding the AI generation of human-like experiences., Comment: Presented at the Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop at CHI 2024

Singular fourth-order Abreu equations have been used to approximate minimizers of convex functionals subject to a convexity constraint in dimensions higher than or equal to two. For Abreu type equations, they often exhibit different solvability phenomena in dimension one and dimensions at least two. We prove the analogues of these results for the variational problem and singular Abreu equations in dimension one, and use the approximation scheme to obtain a characterization of limiting minimizers to the one-dimensional variational problem., Comment: final version to appear in Applied Mathematics and Optimization

Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions. Augmenting LLMs with long-term memory (LTM) presents an opportunity to improve engagement and self-disclosure, but we lack an understanding of how LTM impacts people's interaction with LLM-driven chatbots in public health interventions. We examine the case of CareCall -- an LLM-driven voice chatbot with LTM -- through the analysis of 1,252 call logs and interviews with nine users. We found that LTM enhanced health disclosure and fostered positive perceptions of the chatbot by offering familiarity. However, we also observed challenges in promoting self-disclosure through LTM, particularly around addressing chronic health conditions and privacy concerns. We discuss considerations for LTM integration in LLM-driven chatbots for public health monitoring, including carefully deciding what topics need to be remembered in light of public health goals., Comment: Accepted to ACM CHI 2024 as a full paper

Ultrasound is well-established as an imaging modality for diagnostic and interventional purposes. However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies. Automating the image acquisition task could improve acquisition reproducibility and quality but training such an algorithm requires large amounts of navigation data, not saved in routine examinations. Thus, we propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions, such that this pipeline can later be used by learning algorithms for navigation. We present a novel simulation pipeline which uses segmentations from other modalities, an optimized volumetric data representation and GPU-accelerated Monte Carlo path tracing to generate view-dependent and patient-specific ultrasound images. We extensively validate the correctness of our pipeline with a phantom experiment, where structures' sizes, contrast and speckle noise properties are assessed. Furthermore, we demonstrate its usability to train neural networks for navigation in an echocardiography view classification experiment by generating synthetic images from more than 1000 patients. Networks pre-trained with our simulations achieve significantly superior performance in settings where large real datasets are not available, especially for under-represented classes. The proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated., Comment: 24 pages, 10 figures, 5 tables

In the mental health domain, Large Language Models (LLMs) offer promising new opportunities, though their inherent complexity and low controllability have raised questions about their suitability in clinical settings. We present MindfulDiary, a mobile journaling app incorporating an LLM to help psychiatric patients document daily experiences through conversation. Designed in collaboration with mental health professionals (MHPs), MindfulDiary takes a state-based approach to safely comply with the experts' guidelines while carrying on free-form conversations. Through a four-week field study involving 28 patients with major depressive disorder and five psychiatrists, we found that MindfulDiary supported patients in consistently enriching their daily records and helped psychiatrists better empathize with their patients through an understanding of their thoughts and daily contexts. Drawing on these findings, we discuss the implications of leveraging LLMs in the mental health domain, bridging the technical feasibility and their integration into clinical settings., Comment: 20 pages, 6 figures, 4 tables. Accepted at ACM CHI 2024

Designers rely on visual search to explore and develop ideas in early design stages. However, designers can struggle to identify suitable text queries to initiate a search or to discover images for similarity-based search that can adequately express their intent. We propose GenQuery, a novel system that integrates generative models into the visual search process. GenQuery can automatically elaborate on users' queries and surface concrete search directions when users only have abstract ideas. To support precise expression of search intents, the system enables users to generatively modify images and use these in similarity-based search. In a comparative user study (N=16), designers felt that they could more accurately express their intents and find more satisfactory outcomes with GenQuery compared to a tool without generative features. Furthermore, the unpredictability of generations allowed participants to uncover more diverse outcomes. By supporting both convergence and divergence, GenQuery led to a more creative experience., Comment: 18 pages and 12 figures

By simply composing prompts, developers can prototype novel generative applications with Large Language Models (LLMs). To refine prototypes into products, however, developers must iteratively revise prompts by evaluating outputs to diagnose weaknesses. Formative interviews (N=8) revealed that developers invest significant effort in manually evaluating outputs as they assess context-specific and subjective criteria. We present EvalLM, an interactive system for iteratively refining prompts by evaluating multiple outputs on user-defined criteria. By describing criteria in natural language, users can employ the system's LLM-based evaluator to get an overview of where prompts excel or fail, and improve these based on the evaluator's feedback. A comparative study (N=12) showed that EvalLM, when compared to manual evaluation, helped participants compose more diverse criteria, examine twice as many outputs, and reach satisfactory prompts with 59% fewer revisions. Beyond prompts, our work can be extended to augment model evaluation and alignment in specific application contexts., Comment: Accepted to CHI 2024

Children typically learn to identify and express emotions through sharing their stories and feelings with others, particularly their family. However, it is challenging for parents or siblings to have emotional communication with children since children are still developing their communication skills. We present ChaCha, a chatbot that encourages and guides children to share personal events and associated emotions. ChaCha combines a state machine and large language models (LLMs) to keep the dialogue on track while carrying on free-form conversations. Through an exploratory study with 20 children (aged 8-12), we examine how ChaCha prompts children to share personal events and guides them to describe associated emotions. Participants perceived ChaCha as a close friend and shared their stories on various topics, such as family trips and personal achievements. Based on the findings, we discuss opportunities for leveraging LLMs to design child-friendly chatbots to support children in sharing emotions., Comment: 20 pages, 5 figures, 2 tables; Accepted at ACM CHI 2024. More details at https://naver-ai.github.io/chacha/

A personally tailored exercise regimen is crucial to ensuring sufficient physical activities, yet challenging to create as people have complex schedules and considerations and the creation of plans often requires iterations with experts. We present PlanFitting, a conversational AI that assists in personalized exercise planning. Leveraging generative capabilities of large language models, PlanFitting enables users to describe various constraints and queries in natural language, thereby facilitating the creation and refinement of their weekly exercise plan to suit their specific circumstances while staying grounded in foundational principles. Through a user study where participants (N=18) generated a personalized exercise plan using PlanFitting and expert planners (N=3) evaluated these plans, we identified the potential of PlanFitting in generating personalized, actionable, and evidence-based exercise plans. We discuss future design opportunities for AI assistants in creating plans that better comply with exercise principles and accommodate personal constraints., Comment: 22 pages, 5 figures, 1 table

Extracting semantic representations from mobile user interfaces (UI) and using the representations for designers' decision-making processes have shown the potential to be effective computational design support tools. Current approaches rely on machine learning models trained on small-sized mobile UI datasets to extract semantic vectors and use screenshot-to-screenshot comparison to retrieve similar-looking UIs given query screenshots. However, the usability of these methods is limited because they are often not open-sourced and have complex training pipelines for practitioners to follow, and are unable to perform screenshot set-to-set (i.e., app-to-app) retrieval. To this end, we (1) employ visual models trained with large web-scale images and test whether they could extract a UI representation in a zero-shot way and outperform existing specialized models, and (2) use mathematically founded methods to enable app-to-app retrieval and design consistency analysis. Our experiments show that our methods not only improve upon previous retrieval models but also enable multiple new applications., Comment: AI & HCI Workshop at the ICML 2023

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search "unreasonable" local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and "reasonable" model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable., Comment: 32 pages, 6 figures, 5 tables. Accepted for publication in the Proceedings of the ACM on Human-Computer Interaction (PACM HCI), CSCW 2023

Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system's capabilities via strict user goals, namely "user familiarity" bias. This data bias deepens when it combines with data-driven TOD systems, as it is impossible to fathom the effect of it with existing static evaluations. Hence, we conduct an interactive user study to unveil how vulnerable TOD systems are against realistic scenarios. In particular, we compare users with 1) detailed goal instructions that conform to the system boundaries (closed-goal) and 2) vague goal instructions that are often unsupported but realistic (open-goal). Our study reveals that conversations in open-goal settings lead to catastrophic failures of the system, in which 92% of the dialogues had significant issues. Moreover, we conduct a thorough analysis to identify distinctive features between the two settings through error annotation. From this, we discover a novel "pretending" behavior, in which the system pretends to handle the user requests even though they are beyond the system's capabilities. We discuss its characteristics and toxicity while showing recent large language models can also suffer from this behavior., Comment: NLP4ConvAI Workshop at ACL 2024

Endovascular guidewire manipulation is essential for minimally-invasive clinical applications (Percutaneous Coronary Intervention (PCI), Mechanical thrombectomy techniques for acute ischemic stroke (AIS), or Transjugular intrahepatic portosystemic shunt (TIPS)). All procedures commonly require 3D vessel geometries from 3D CTA (Computed Tomography Angiography) images. During these procedures, the clinician generally places a guiding catheter in the ostium of the relevant vessel and then manipulates a wire through the catheter and across the blockage. The clinician only uses X-ray fluoroscopy intermittently to visualize and guide the catheter, guidewire, and other devices. However, clinicians still passively control guidewires/catheters by relying on limited indirect observation (i.e., 2D partial view of devices, and intermittent updates due to radiation limit) from X-ray fluoroscopy. Modeling and controlling the guidewire manipulation in coronary vessels remains challenging because of the complicated interaction between guidewire motions with different physical properties (i.e., loads, coating) and vessel geometries with lumen conditions resulting in a highly non-linear system. This paper introduces a scalable learning pipeline to train AI-based agent models toward automated endovascular predictive device controls. First, we create a scalable environment by pre-processing 3D CTA images, providing patient-specific 3D vessel geometry and the centerline of the coronary. Next, we apply a large quantity of randomly generated motion sequences from the proximal end to generate wire states associated with each environment using a physics-based device simulator. Then, we reformulate the control problem to a sequence-to-sequence learning problem, in which we use a Transformer-based model, trained to handle non-linear sequential forward/inverse transition functions.

Sighted and blind and low vision (BLV) creators alike use videos to communicate with broad audiences. Yet, video editing remains inaccessible to BLV creators. Our formative study revealed that current video editing tools make it difficult to access the visual content, assess the visual quality, and efficiently navigate the timeline. We present AVscript, an accessible text-based video editor. AVscript enables BLV creators to edit their video using a script that embeds the video's visual content, visual errors (e.g., dark or blurred footage), and speech. BLV creators can use AVscript to efficiently navigate between scenes and visual errors or to locate objects in the frame or spoken words of interest. A comparison study (N=12) showed that AVscript significantly lowered BLV creators' mental demands while increasing confidence and independence in video editing. We further demonstrate the potential of AVscript through an exploratory study (N=3) where BLV creators edited their own footage., Comment: CHI 2023

Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on naturalistic conversations while pursuing a given goal, such as collecting self-report data from users. We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably. To this aim, we formulated four prompt designs with different structures and personas. Through an online study (N = 48) where participants conversed with chatbots driven by different designs of prompts, we assessed how prompt designs and conversation topics affected the conversation flows and users' perceptions of chatbots. Our chatbots covered 79% of the desired information slots during conversations, and the designs of prompts and topics significantly influenced the conversation flows and the data collection performance. We discuss the opportunities and challenges of building chatbots with LLMs., Comment: 22 pages including Appendix, 7 figures, 7 tables. Accepted to PACM HCI (CSCW 2024)

This work tackles practical issues which arise when using a tendon-driven robotic manipulator (TDRM) with a long, flexible, passive proximal section in medical applications. Tendon-driven devices are preferred in medicine for their improved outcomes via minimally invasive procedures, but TDRMs come with unique challenges such as sterilization and reuse, simultaneous control of tendons, hysteresis in the tendon-sheath mechanism, and unmodeled effects of the proximal section shape. A separable TDRM which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. An open-loop redundant controller which resolves the redundancy in the kinematics is developed. Simple linear hysteresis compensation and re-tension compensation based on the physical properties of the device are proposed. The controller and compensation methods are evaluated on a testbed for a straight proximal section, a curved proximal section at various static angles, and a proximal section which dynamically changes angles; and overall, distal tip error was reduced.

We study the solvability of singular Abreu equations which arise in the approximation of convex functionals subject to a convexity constraint. Previous works established the solvability of their second boundary value problems either in two dimensions, or in higher dimensions under either a smallness condition or a radial symmetry condition. Here, we solve the higher dimensional case by transforming singular Abreu equations into linearized Monge-Amp\`ere equations with drifts. We establish global H\"older estimates for the linearized Monge-Amp\`ere equation with drifts under suitable hypotheses, and then use them to the regularity and solvability of the second boundary value problem for singular Abreu equations in higher dimensions. Many cases with general right-hand side will also be discussed., Comment: v2: final version to appear in JEMS

Current natural language interaction for self-tracking tools largely depends on bespoke implementation optimized for a specific tracking theme and data format, which is neither generalizable nor scalable to a tremendous design space of self-tracking. However, training machine learning models in the context of self-tracking is challenging due to the wide variety of tracking topics and data formats. In this paper, we propose a novel NLP task for self-tracking that extracts close- and open-ended information from a retrospective activity log described as a plain text, and a domain-agnostic, GPT-3-based NLU framework that performs this task. The framework augments the prompt using synthetic samples to transform the task into 10-shot learning, to address a cold-start problem in bootstrapping a new tracking topic. Our preliminary evaluation suggests that our approach significantly outperforms the baseline QA models. Going further, we discuss future application domains toward which the NLP and HCI researchers can collaborate., Comment: Accepted to NAACL '22 2nd Workshop on Bridging Human-Computer Interaction and Natural Language Processing. 10 pages including appendix, 2 figures, and 1 table

Clinicians require substantial training and experience to become comfortable with steering Intracardiac echocardiography (ICE) catheter to localize and measure the area of treatment to watch for complications while device catheters are deployed in another access. Thus, it is reasonable that a robotic-assist system to hold and actively manipulate the ICE catheter could ease the workload of the physician. Existing commercially-available robotic systems and research prototypes all use existing commercially available ICE catheters based on multiple tendon-sheath mechanism (TSM). To motorize the existing TSM-based ICE catheter, the actuators interface with the outer handle knobs to manipulate four internal tendons. However, in practice, the actuators are located at a sterile, safe place far away from the ICE handle. Thus, to interface with knobs, there exist multiple coupled gear structures between two, leading to a highly nonlinear behavior (e.g. various slack, elasticity) alongside hysteresis phenomena in TSM. Since ICE catheters are designed for single use, the expensive actuators need to be located in a safe place so as to be reusable. Moreover, these actuators should interface as directly as possible with the tendons for accurate tip controls. In this paper, we introduce a separable ICE catheter robot with four tendon actuation: one part reusable and another disposable. Moreover, we propose a practical model and calibration method for our proposed mechanism so that four tendons are actuated simultaneously allowing for precise tip control and mitigating issues with conventional devices such as dead-zone and hysteresis with simple linear compensation. We consider an open-loop controller since many available ICE catheters are used without position-tracking sensors at the tip due to costs and single use

Current activity tracking technologies are largely trained on younger adults' data, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies., Comment: To appear at ACM CHI 2022. 21 pages, 3 figures, 7 tables. For the NSF funded project, visit https://mymove-collective.github.io

Purpose: Intra-Cardiac Echocardiography (ICE) is a powerful imaging modality for guiding cardiac electrophysiology and structural heart interventions. ICE provides real-time observation of anatomy and devices, while enabling direct monitoring of potential complications. In single operator settings, the physician needs to switch back-and-forth between the ICE catheter and therapy device, making continuous ICE support impossible. Two operators setup are therefore sometimes implemented, with the challenge of increase room occupation and cost. Two operator setups are sometimes implemented, but increase procedural costs and room occupation. Methods: ICE catheter robotic control system is developed with automated catheter tip repositioning (i.e. view recovery) method, which can reproduce important views previously navigated to and saved by the user. The performance of the proposed method is demonstrated and evaluated in a combination of heart phantom and animal experiments. Results: Automated ICE view recovery achieved catheter tip position accuracy of 2.09 +/-0.90 mm and catheter image orientation accuracy of 3.93 +/- 2.07 degree in animal studies, and 0.67 +/- 0.79 mm and 0.37 +/- 0.19 degree in heart phantom studies, respectively. Our proposed method is also successfully used during transeptal puncture in animals without complications, showing the possibility for fluoro-less transeptal puncture with ICE catheter robot. Conclusion: Robotic ICE imaging has the potential to provide precise and reproducible anatomical views, which can reduce overall execution time, labor burden of procedures, and x-ray usage for a range of cardiac procedures. Keywords: Automated View Recovery, Path Planning, Intra-cardiac echocardiography (ICE), Catheter, Tendon-driven manipulator, Cardiac Imaging, Comment: arXiv admin note: substantial text overlap with arXiv:2009.05859

Position sensitive detectors (PSDs) offer possibility to track single active marker's two (or three) degrees of freedom (DoF) position with a high accuracy, while having a fast response time with high update frequency and low latency, all using a very simple signal processing circuit. However they are not particularly suitable for 6-DoF object pose tracking system due to lack of orientation measurement, limited tracking range, and sensitivity to environmental variation. We propose a novel 6-DoF pose tracking system for a rigid object tracking requiring a single active marker. The proposed system uses a stereo-based PSD pair and multiple Inertial Measurement Units (IMUs). This is done based on a practical approach to identify and control the power of Infrared-Light Emitting Diode (IR-LED) active markers, with an aim to increase the tracking work space and reduce the power consumption. Our proposed tracking system is validated with three different work space sizes and for static and dynamic positional accuracy using robotic arm manipulator with three different dynamic motion patterns. The results show that the static position root-mean-square (RMS) error is 0.6mm. The dynamic position RMS error is 0.7-0.9mm. The orientation RMS error is between 0.04 and 0.9 degree at varied dynamic motion. Overall, our proposed tracking system is capable of tracking a rigid object pose with sub-millimeter accuracy at the mid range of the work space and sub-degree accuracy for all work space under a lab setting.

Tendon-driven continuum manipulators (TDCM) are commonly used in minimally invasive surgical systems due to their long, thin, flexible structure that is compliant in narrow or tortuous environments. There exist many researches for precise tip control of the articulating section. However, these models do not account for the proximal shaft shape of TDCM, affecting the tip controls in practical settings. In this paper, we propose a gradient-based shift detection method based on motor current that can easily find the offset of task space models (i.e., hysteresis). We analyze our proposed methods with multiple Intra-cardiac Echocardiography catheters, which are typical commercial example of TDCM. Our results show that the errors from varied proximal shape are considerably reduced, and the accuracy of the tip manipulation is improved when changing external environmental structures.