About

Dr. Lisa Anthony is an Associate Professor in the Department of Computer & Information Science & Engineering (CISE) at the University of Florida in Gainesville, FL. She teaches courses in the Human-Centered Computing (HCC) graduate program as well as the Digital Arts & Sciences (DAS) undergraduate and graduate programs. Her research focuses on understanding, designing, and developing natural user interactions (NUI), particularly for children and families. NUIs enable users to interact with technology through human abilities such as touch, voice, vision, and motion. Dr. Anthony's work addresses the unique design challenges and opportunities presented by children's developing cognitive and physical capabilities. Her research integrates and contributes to fields including human-computer interaction, child-computer interaction, multimodal interaction, machine learning and artificial intelligence, cognitive science, and interaction design. She aims to understand children's expectations and abilities regarding NUIs and to design new multimodal interfaces for children in contexts such as education, healthcare, and serious games. Recent projects also explore human-AI interaction for these users and contexts. Before joining the University of Florida, Dr. Anthony was a Research Assistant Professor in the Department of Information Systems at the University of Maryland-Baltimore County (UMBC). She earned her Ph.D. from the Human Computer Interaction Institute (HCII) in the School of Computer Science at Carnegie Mellon University, where her thesis focused on handwriting-based interfaces for intelligent tutoring systems in algebra equation-solving. Her advisors were Jie Yang and Ken Koedinger. She also holds an M.S. in HCI from Carnegie Mellon and a B.S. and M.S. in computer science with concentrations in artificial intelligence, human-computer interaction, and software engineering from Drexel University. Her earlier research included developing a computer-aided conceptual design tool for engineers and using genetic programming to evolve board evaluation functions for computer game agents. Dr. Anthony has industry experience at Lockheed Martin Advanced Technology Laboratories and Fuji-Xerox Palo Alto Laboratory, working on advanced user interface technologies such as spoken-language understanding, handwriting recognition, gesture recognition, and collaborative exploratory search.

Research topics

• Computer Science
• Psychology
• Machine Learning
• Artificial Intelligence
• Speech recognition
• Multimedia
• Human–computer interaction
• Engineering

Selected publications

• Interactive Segmentation With Prototype Learning for Few-Shot Root Annotation

  IEEE Transactions on Geoscience and Remote Sensing · 2025-01-01

  article

  Fine-scale pixel-level annotation of minirhizotron root images is a less common and challenging task. We present an interactive segmentation framework to accelerate root annotation. We leverage the concept of few-shot segmentation so that the pretrained model can be effectively fine-tuned and transferred to an unseen category. To provide immediate feedback for real-time interaction, we adapted a UNet architecture by attaching lightweight embedding layers which leveraged a prototype learning (PL) approach to efficiently learn the data metric in the embedding space. The prototypes optimized by the prototype loss preserve the within-class data variation, enabling effective fine-tuning. Furthermore, we designed a system with our interactive annotation framework and experimented with real users to validate the approach.

  PublisherDOI

• ‘Over Time Everyone’s Gonna Be Open to It’: User Attitudes Toward Security and Privacy in Continuous Authentication for Smart Homes

  IEEE Access · 2025-01-01 · 1 citations

  articleOpen accessSenior author

  Continuous authentication (CA), a user authentication approach that continuously verifies a person’s identity without requiring explicit input, is increasingly being deployed in smart homes to maintain security posture throughout user sessions. However, prior research has overlooked user attitudes toward the increased data collection and surveillance associated with CA in smart homes. To bridge this gap, we conducted a focus group study with 33 participants, using a design probe video to simulate various CA implementation scenarios in smart homes. We explored participants’ current authentication methods (e.g., passwords and physiological biometrics) and examined their perceptions of CA. Through affinity diagramming, we found that participants perceive smart-home CA as presenting privacy and security challenges yet possessing great potential for enhanced usability. Participants also envisioned CA systems that offer more granular permission controls over personal data. Our findings indicate the contextual dependencies in balancing usability with privacy and security concerns. Our contributions include a comprehensive empirical dataset featuring the design probe video, participant transcripts, and a conceptual model of users’ nuanced understanding. We provide design recommendations for smart-home CA systems, emphasizing transparency as a crucial factor in building user trust and improving adoption rates.

  PublisherDOI

• Adaptive vs Monthly Support for Weight-Loss Maintenance

  JAMA Network Open · 2025-09-22 · 1 citations

  articleOpen access

  Importance: Weight regain is common after the end of initial weight-loss treatment. Existing extended care programs (which typically provide sessions once per month) improve long-term weight-loss outcomes, but with only modest effect vs control. Objective: To determine whether weight regain is reduced via provision of telephone-based extended care on an adaptive (triggered by a study algorithm that estimates when individuals are at high risk for weight regain) vs static (provided once per month) schedule. Design, Setting, and Participants: A randomized clinical trial conducted between October 2019 and November 2024 in areas within driving distance of Gainesville, Florida. Adults with obesity enrolled in a 16-week weight-loss program; those who lost 5% or more of their baseline weight were eligible for randomization to 1 of 2 maintenance care conditions. Intervention: Participants received 20 months of telephone-based extended care support, delivered individually by a trained interventionist, either on an adaptive or static schedule. Main Outcomes and Measures: The primary outcome was weight change from month 4 (end of initial intervention) to month 24. Results: The 255 participants (mean [SD] age, 50.6 [11.3] years; 209 [82.0%] women; 51 [20.0%] Black, 25 [9.8%] Hispanic or Latino, and 170 [66.7%] non-Hispanic White) were randomized to an adaptive (128 participants) or static (127 participants) group. Weight regain from month 4 to 24 was 1.27 (95% CI, 0.07 to 2.47) kg in the adaptive vs 1.75 (95% CI, 0.43 to 3.06) kg in the static group, with no difference by condition. At month 24, adaptive participants maintained a mean (SD) weight loss of 8.1% (7.8%) from initial intervention baseline while static participants maintained 7.9% (8.5%); there was not a significant difference in the proportion of participants maintaining weight loss of 5% or more (adaptive, 59.5%; static, 59.8%). Conclusions and relevance: In this randomized clinical trial, participants in both conditions were successful at maintaining initial weight loss 20 months after the end of a weight-loss program, but providing extended care on an adaptive schedule did not confer additional benefit vs the once-per-month static schedule. Future research should investigate whether more precise algorithms of high-risk periods for weight regain can be developed and whether these models can improve weight maintenance outcomes. Moreover, given success of participants in both conditions at maintaining initial weight loss, future research should also investigate methods of improving the implementation and dissemination potential of telephone-based extended care interventions. Trial Registration: ClinicalTrials.gov Identifier: NCT04116853.

  PublisherOA PDFDOI

• How Hand Constraints Influence User Defined Gestures in Mixed Reality

  2025-05-26

  article

  How do user-defined gestures for mixed reality change when users’ hands are engaged in tasks? To address this question, we conducted a gesture elicitation study to understand user preferences and the characteristics of gestures conceptualized in three scenarios with varying levels of hand constraints, namely: "both hands free", "one hand fixed", and "both hands busy". We analyzed these gestures across multiple dimensions and compared our findings with those from prior research. Our results indicate that when both hands are occupied, users tend to favor head gestures over those involving other body parts, such as the eyes or legs. Additionally, we found that most of the proposed gestures were metaphorical, with many influenced by legacy bias. These insights enhance our understanding of how hand constraints influence gesture choices in mixed reality scenarios.

  PublisherDOI

• MyTrack+: Human-centered design of an mHealth app to support long-term weight loss maintenance

  Frontiers in Digital Health · 2024-04-22 · 5 citations

  articleOpen accessSenior author

  A growing body of research has focused on the utility of adaptive intervention models for promoting long-term weight loss maintenance; however, evaluation of these interventions often requires customized smartphone applications. Building such an app from scratch can be resource-intensive. To support a novel clinical trial of an adaptive intervention for weight loss maintenance, we developed a companion app, MyTrack+, to pair with a main commercial app, FatSecret (FS), leveraging a user-centered design process for rapid prototyping and reducing software engineering efforts. MyTrack+ seamlessly integrates data from FS and the BodyTrace smart scale, enabling participants to log and self-monitor their health data, while also incorporating customized questionnaires and timestamps to enhance data collection for the trial. We iteratively refined the app by first developing initial mockups and incorporating feedback from a usability study with 17 university students. We further improved the app based on an in-the-wild pilot study with 33 participants in the target population, emphasizing acceptance, simplicity, customization options, and dual app usage. Our work highlights the potential of using an iterative human-centered design process to build a companion app that complements a commercial app for rapid prototyping, reducing costs, and enabling efficient research progress.

  PublisherOA PDFDOI

• Investigating Contextual Notifications to Drive Self-Monitoring in mHealth Apps for Weight Maintenance

  2024-05-11 · 18 citations

  articleOpen accessSenior author

  Mobile health applications for weight maintenance offer self-monitoring as a tool to empower users to achieve health goals (e.g., losing weight); yet maintaining consistent self-monitoring over time proves challenging for users. These apps use push notifications to help increase users’ app engagement and reduce long-term attrition, but they are often ignored by users due to appearing at inopportune moments. Therefore, we analyzed whether delivering push notifications based on time alone or also considering user context (e.g., current activity) affected users’ engagement in a weight maintenance app, in a 4-week in-the-wild study with 30 participants. We found no difference in participants’ overall (across the day) self-monitoring frequency between the two conditions, but in the context-based condition, participants responded faster and more frequently to notifications, and logged their data more timely (as eating/exercising occurs). Our work informs the design of notifications in weight maintenance apps to improve their efficacy in promoting self-monitoring.

  PublisherOA PDFDOI

• Design of Project STAR: A randomized controlled trial evaluating the impact of an adaptive intervention on long-term weight-loss maintenance

  Contemporary Clinical Trials · 2024-10-09 · 6 citations

  articleOpen access
  PublisherOA PDFDOI

• Deep-Accel: A Face Touch Prediction Framework to Reduce Obsessive-Compulsive Disorder

  2024-08-07 · 1 citations

  article

  Obsessive-compulsive disorder (OCD) can come in multiple types among human beings. An important type of OCD is touching the face intentionally / unintentionally, particularly when in deep thinking. Examples include pulling hair, rubbing eyes, pinching pimples, vaping, and scratching the chin. Such a constant urge can easily transmit germs and may also become vulnerable for disabled and older adults. Although there are several work that uses machine learning algorithms to identify human activities, there is very limited work on detecting face touch with the raw accelerometer data. There are multiple challenges to using accelerometer data for machine learning algorithms like noise and data dimensionality. In this paper, we introduce a deep learning framework Deep-Accel to predict face touching activities compared to other human activities. We give an in-depth analysis of the proposed framework and show that it achieves up to 79.6% accuracy in classifying face touching activities and up to 73.2% in classifying all activities.

  PublisherDOI

• Elicitating Challenges and User Needs Associated with Annotation Software for Plant Phenotyping

  2024-03-18 · 3 citations

  articleOpen access

  Artificial Intelligence (AI) has been enhancing data analysis efficiency and accuracy during plant phenotyping, which is vital for tackling global agricultural and environmental challenges. Designing a reliable AI system to assist precise plant phenotyping begins with high-quality phenotypic feature annotation, which usually involves collaboration between plant scientists and AI specialists. However, due to the high level of diversity in these researchers’ backgrounds, it is likely that they have differing user needs from a fine-grained plant feature annotation system. We conducted semi-structured interviews with eight experienced annotators from diverse backgrounds, and observed how they interact with their preferred annotation system, to elucidate the challenges faced when annotating plant features and identify user needs. We collected qualitative responses to the interview questions, and conducted a quantitative evaluation of the agreement of their annotations on the given images. By analyzing the participants’ behaviors and the collected data, we identified common user needs and derived implications for the design of an AI-assisted annotation system, including providing a range of annotation options, the flexibility to adapt annotations, and functions to help addressing uncertainty. Our research contributes to the design of systems that make annotations efficient and reliable, not only benefiting plant phenotyping, but also other interdisciplinary fields that rely on user-driven annotations.

  PublisherOA PDFDOI

• Understanding User Needs for Task Guidance Systems Through the Lens of Cooking

  Designing Interactive Systems Conference · 2024-06-29 · 4 citations

  article

  To design intuitive and effective context-aware task guidance systems, we must understand users’ thought processes and the obstacles they experience when they perform tasks. Though task guidance systems have proven beneficial in many domains for improving task performance and reducing user frustration, there is a lack of general guidelines and design principles for their development. Prior work has shown that recipe-based cooking is a strong medium for studying task planning and execution. In response, we conducted a contextual inquiry study in home kitchens, observing eight different participants’ cooking sessions. We used affinity diagramming of our notes and transcripts to identify common obstacles faced by participants and establish user needs in the areas of object interaction, safety, knowledge base, and task coordination. We discuss how these findings can inform the design of technology-driven solutions for task guidance systems beyond cooking.

  PublisherDOI

Recent grants

• HCC: Small: Collaborative Research: Mobile Gesture Interaction for Kids: Sensing, Recognition, and Error Recovery

  NSF · $234k · 2012–2014

• CAREER: Natural User Interfaces for Children

  NSF · $502k · 2016–2021

• HCC: Small: Collaborative Research: Mobile Gesture Interaction for Kids: Sensing, Recognition, and Error Recovery

  NSF · $213k · 2013–2017

Frequent coauthors

• Jaime Ruiz

  18 shared

• Julia Woodward

  University of South Florida

  16 shared

• Aishat Aloba

  15 shared

• Nikita Soni

  13 shared

• Alex Shaw

  12 shared

• Kathryn A. Stofer

  University of Florida

  12 shared

• Jacob O. Wobbrock

  Defense Information School

  9 shared

• Kenneth R. Koedinger

  Carnegie Mellon University

  8 shared

Labs

• Intelligent Natural Interaction Technology (INIT) LaboratoryPI

Awards & honors

• UF Term Professorship, 2019
• ACM Senior Member, 2019
• NSF CAREER Award, 2016-2020
• HWCOE Undergraduate Faculty Adviser/Mentor of the Year, 2017…
• best paper awards, including one in 2013 at the ACM SIGCHI C…