arXiv subjects

Lawrence H. Kim

Publications and source records attributed to Lawrence H. Kim.

6 recordsLinked to original sources

arXiv2026

How Neurotypical and Autistic Children Interact Nonverbally with Anthropomorphic Agents in Open-Ended Tasks

What nonverbal behaviors should a robot respond to? Understanding how children-both neurotypical and autistic-engage with embodied artificial agents is critical for developing inclusive and socially interactive systems. In this paper, we study "open-ended" unconstrained interactions with embodied agents, where little is known about how children behave nonverbally when given few instructions. We conducted a Wizard-of-Oz study in which children were invited to interact nonverbally with 6 different embodied virtual characters displayed on a television screen. We collected 563 (141 unique) nonverbal behaviors produced by children and compare the childre's interaction patterns with those previously reported in an adult study. We also report the presence of repetitive face and hand movements, which should be considered in the development of nonverbally interactive artificial agents.

cs.HC↗

arXiv2025

Open WebUI: An Open, Extensible, and Usable Interface for AI Interaction

While LLMs enable a range of AI applications, interacting with multiple models and customizing workflows can be challenging, and existing LLM interfaces offer limited support for collaborative extension or real-world evaluation. In this work, we present an interface toolkit for LLMs designed to be open (open-source and local), extensible (plugin support and users can interact with multiple models), and usable. The extensibility is enabled through a two-pronged plugin architecture and a community platform for sharing, importing, and adapting extensions. To evaluate the system, we analyzed organic engagement through social platforms, conducted a user survey, and provided notable examples of the toolkit in the wild. Through studying how users engage with and extend the toolkit, we show how extensible, open LLM interfaces provide both functional and social value, and highlight opportunities for future HCI work on designing LLM toolkit platforms and shaping local LLM-user interaction.

cs.HC↗

arXiv2025

DiminishAR: Diminishing Visual Distractions via Holographic AR Displays

Smartphones are integral to modern life, yet research highlights the cognitive drawbacks associated with their mere presence. While physically removing them can mitigate these effects, it is often inconvenient and may heighten anxiety due to prolonged separation. To address this, we use holographic augmented reality (AR) displays to visually diminish distractions with two interventions: 1) Visual Camouflage, which disguises the smartphone with a hologram that matches its size and blends with the background, making it less noticeable, and 2) Visual Substitution, which occludes the smartphone with a contextually relevant hologram, like books on a desk. In a study with 60 participants, we compared cognitive performance with the smartphone nearby, remote, and visually diminished by our AR interventions. Our findings show that the interventions significantly reduce cognitive impairment, with effects comparable to physically removing the smartphone. The adaptability of our approach opens new avenues to manage visual distractions in daily life.

cs.HC↗

arXiv2024

React to This! How Humans Challenge Interactive Agents using Nonverbal Behaviors

How do people use their faces and bodies to test the interactive abilities of a robot? Making lively, believable agents is often seen as a goal for robots and virtual agents but believability can easily break down. In this Wizard-of-Oz (WoZ) study, we observed 1169 nonverbal interactions between 20 participants and 6 types of agents. We collected the nonverbal behaviors participants used to challenge the characters physically, emotionally, and socially. The participants interacted freely with humanoid and non-humanoid forms: a robot, a human, a penguin, a pufferfish, a banana, and a toilet. We present a human behavior codebook of 188 unique nonverbal behaviors used by humans to test the virtual characters. The insights and design strategies drawn from video observations aim to help build more interaction-aware and believable robots, especially when humans push them to their limits.

cs.HC↗

arXiv2021

Linear Predictive Coding for Acute Stress Prediction from Computer Mouse Movements

Prior work demonstrated the potential of using the Linear Predictive Coding (LPC) filter to approximate muscle stiffness and damping from computer mouse movements to predict acute stress levels of users. Theoretically, muscle stiffness and damping in the arm can be estimated using a mass-spring-damper (MSD) biomechanical model. However, the damping frequency (i.e., stiffness) and damping ratio values derived using LPC were not yet compared with those from a theoretical MSD model. This work demonstrates that the damping frequency and damping ratio from LPC are significantly correlated with those from an MSD model, thus confirming the validity of using LPC to infer muscle stiffness and damping. We also compare the stress level binary classification performance using the values from LPC and MSD with each other and with neural network-based baselines. We found comparable performance across all conditions demonstrating LPC and MSD model-based stress prediction efficacy, especially for longer mouse trajectories. Clinical relevance: This work demonstrates the validity of the LPC filter to approximate muscle stiffness and damping and predict acute stress from computer mouse movements.

eess.SP↗

arXiv2019

Interaction with Ubiquitous Robots and Autonomous IoT

Robotics have been slowly permeating Internet of Things (IoT) where the previously ubiquitous but static sensors are now given the power to actively navigate the environment and even interact with users. Emergence of these ubiquitous swarms of robots not only opens up the range of possible applications, but also increases the number of elements to study and design for. We do not yet understand how, when, and where these robots should move, manipulate, and touch around people. Through user-centered studies, we aim to better understand how to best design for interaction with Autonomous IoT or a swarm of ubiquitous robots.

cs.HC↗