arXiv subjects

Per Ola Kristensson

Publications and source records attributed to Per Ola Kristensson.

At least 37 records · Page 2Linked to original sources

arXiv2026

LocoScooter: Designing a Stationary Scooter-Based Locomotion System for Navigation in Virtual Reality

Virtual locomotion remains a challenge in VR, especially in space-limited environments where room-scale walking is impractical. We present LocoScooter, a low-cost, deployable locomotion interface combining foot-sliding on a compact treadmill with handlebar steering inspired by scooter riding. Built from commodity hardware, it supports embodied navigation through familiar, physically engaging movement. In a within-subject study (N = 14), LocoScooter significantly improved immersion, enjoyment, and bodily involvement over joystick navigation, while maintaining comparable efficiency and usability. Despite higher physical demand, users did not report increased fatigue, suggesting familiar movements can enrich VR navigation.

cs.HC↗

arXiv2025

ImageTalk: Designing a Multimodal AAC Text Generation System Driven by Image Recognition and Natural Language Generation

People living with Motor Neuron Disease (plwMND) frequently encounter speech and motor impairments that necessitate a reliance on augmentative and alternative communication (AAC) systems. This paper tackles the main challenge that traditional symbol-based AAC systems offer a limited vocabulary, while text entry solutions tend to exhibit low communication rates. To help plwMND articulate their needs about the system efficiently and effectively, we iteratively design and develop a novel multimodal text generation system called ImageTalk through a tailored proxy-user-based and an end-user-based design phase. The system demonstrates pronounced keystroke savings of 95.6%, coupled with consistent performance and high user satisfaction. We distill three design guidelines for AI-assisted text generation systems design and outline four user requirement levels tailored for AAC purposes, guiding future research in this field.

cs.HC↗

arXiv2025

X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding

Long-form egocentric video understanding provides rich contextual information and unique insights into long-term human behaviors, holding significant potential for applications in embodied intelligence, long-term activity analysis, and personalized assistive technologies. However, existing benchmark datasets primarily focus on single, short (\eg, minutes to tens of minutes) to moderately long videos, leaving a substantial gap in evaluating extensive, ultra-long egocentric video recordings. To address this, we introduce X-LeBench, a novel benchmark dataset meticulously designed to fill this gap by focusing on tasks requiring a comprehensive understanding of extremely long egocentric video recordings. Our X-LeBench develops a life-logging simulation pipeline that produces realistic, coherent daily plans aligned with real-world video data. This approach enables the flexible integration of synthetic daily plans with real-world footage from Ego4D-a massive-scale egocentric video dataset covers a wide range of daily life scenarios-resulting in 432 simulated video life logs spanning from 23 minutes to 16.4 hours. The evaluations of several baseline systems and multimodal large language models (MLLMs) reveal their poor performance across the board, highlighting the inherent challenges of long-form egocentric video understanding, such as temporal localization and reasoning, context aggregation, and memory retention, and underscoring the need for more advanced models.

cs.CV↗

arXiv2025

The Argument is the Explanation: Structured Argumentation for Trust in Agents

Humans are black boxes -- we cannot observe their neural processes, yet society functions by evaluating verifiable arguments. AI explainability should follow this principle: stakeholders need verifiable reasoning chains, not mechanistic transparency. We propose using structured argumentation to provide a level of explanation and verification neither interpretability nor LLM-generated explanation is able to offer. Our pipeline achieves state-of-the-art 94.44 macro F1 on the AAEC published train/test split (5.7 points above prior work) and $0.81$ macro F1, $\sim$0.07 above previous published results with comparable data setups, for Argumentative MicroTexts relation classification, converting LLM text into argument graphs and enabling verification at each inferential step. We demonstrate this idea on multi-agent risk assessment using the Structured What-If Technique, where specialized agents collaborate transparently to carry out risk assessment otherwise achieved by humans alone. Using Bipolar Assumption-Based Argumentation, we capture support/attack relationships, thereby enabling automatic hallucination detection via fact nodes attacking arguments. We also provide a verification mechanism that enables iterative refinement through test-time feedback without retraining. For easy deployment, we provide a Docker container for the fine-tuned AMT model, and the rest of the code with the Bipolar ABA Python package on GitHub.

cs.LG↗

arXiv2025

Prompt-Driven Agentic Video Editing System: Autonomous Comprehension of Long-Form, Story-Driven Media

Creators struggle to edit long-form, narrative-rich videos not because of UI complexity, but due to the cognitive demands of searching, storyboarding, and sequencing hours of footage. Existing transcript- or embedding-based methods fall short for creative workflows, as models struggle to track characters, infer motivations, and connect dispersed events. We present a prompt-driven, modular editing system that helps creators restructure multi-hour content through free-form prompts rather than timelines. At its core is a semantic indexing pipeline that builds a global narrative via temporal segmentation, guided memory compression, and cross-granularity fusion, producing interpretable traces of plot, dialogue, emotion, and context. Users receive cinematic edits while optionally refining transparent intermediate outputs. Evaluated on 400+ videos with expert ratings, QA, and preference studies, our system scales prompt-driven editing, preserves narrative coherence, and balances automation with creator control.

cs.AI↗

arXiv2025

Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI

Designing effective reward functions is critical for reinforcement learning-based biomechanical simulations, yet HCI researchers and practitioners often waste (computation) time with unintuitive trial-and-error tuning. This paper demystifies reward function design by systematically analyzing the impact of effort minimization, task completion bonuses, and target proximity incentives on typical HCI tasks such as pointing, tracking, and choice reaction. We show that proximity incentives are essential for guiding movement, while completion bonuses ensure task success. Effort terms, though optional, help refine motion regularity when appropriately scaled. We perform an extensive analysis of how sensitive task success and completion time depend on the weights of these three reward components. From these results we derive practical guidelines to create plausible biomechanical simulations without the need for reinforcement learning expertise, which we then validate on remote control and keyboard typing tasks. This paper advances simulation-based interaction design and evaluation in HCI by improving the efficiency and applicability of biomechanical user modeling for real-world interface development.

cs.HC↗

arXiv2025

Bend It, Aim It, Tap It: Designing an On-Body Disambiguation Mechanism for Curve Selection in Mixed Reality

Object selection in Mixed Reality (MR) becomes particularly challenging in dense or occluded environments, where traditional mid-air ray-casting often leads to ambiguity and reduced precision. We present two complementary techniques: (1) a real-time Bezier Curve selection paradigm guided by finger curvature, enabling expressive one-handed trajectories, and (2) an on-body disambiguation mechanism that projects the four nearest candidates onto the user's forearm via proximity-based mapping. Together, these techniques combine flexible, user-controlled selection with tactile, proprioceptive disambiguation. We evaluated their independent and joint effects in a 2x2 within-subjects study (N = 24), crossing interaction paradigm (Bezier Curve vs. Linear Ray) with interaction medium (Mid-air vs. On-body). Results show that on-body disambiguation significantly reduced selection errors and physical demand while improving perceived performance, hedonic quality, and user preference. Bezier input provided effective access to occluded targets but incurred longer task times and greater effort under some conditions. We conclude with design implications for integrating curved input and on-body previews to support precise, adaptive selection in immersive environments.

cs.HC↗

arXiv2025

Mind & Motion: Opportunities and Applications of Integrating Biomechanics and Cognitive Models in HCI

Computational models of how users perceive and act within a virtual or physical environment offer enormous potential for the understanding and design of user interactions. Cognition models have been used to understand the role of attention and individual preferences and beliefs on human decision making during interaction, while biomechanical simulations have been successfully applied to analyse and predict physical effort, fatigue, and discomfort. The next frontier in HCI lies in connecting these models to enable robust, diverse, and representative simulations of different user groups. These embodied user simulations could predict user intents, strategies, and movements during interaction more accurately, benchmark interfaces and interaction techniques in terms of performance and ergonomics, and guide adaptive system design. This UIST workshop explores ideas for integrating computational models into HCI and discusses use cases such as UI/UX design, automated system testing, and personalised adaptive interfaces. It brings researchers from relevant disciplines together to identify key opportunities and challenges as well as feasible next steps for bridging mind and motion to simulate interactive user behaviour.

cs.HC↗

arXiv2025

ReachVox: Clutter-free Reachability Visualization for Robot Motion Planning in Virtual Reality

Human-Robot-Collaboration can enhance workflows by leveraging the mutual strengths of human operators and robots. Planning and understanding robot movements remain major challenges in this domain. This problem is prevalent in dynamic environments that might need constant robot motion path adaptation. In this paper, we investigate whether a minimalistic encoding of the reachability of a point near an object of interest, which we call ReachVox, can aid the collaboration between a remote operator and a robotic arm in VR. Through a user study (n=20), we indicate the strength of the visualization relative to a point-based reachability check-up.

cs.HC↗

arXiv2025

Handows: A Palm-Based Interactive Multi-Window Management System in Virtual Reality

Window management in virtual reality (VR) remains a challenging task due to the spatial complexity and physical demands of current interaction methods. We introduce Handows, a palm-based interface that enables direct manipulation of spatial windows through familiar smartphone-inspired gestures on the user's non-dominant hand. Combining ergonomic layout design with body-centric input and passive haptics, Handows supports four core operations: window selection, closure, positioning, and scaling. We evaluate Handows in a user study (N=15) against two common VR techniques (virtual hand and controller) across these core window operations. Results show that Handows significantly reduces physical effort and head movement while improving task efficiency and interaction precision. A follow-up case study (N=8) demonstrates Handows' usability in realistic multitasking scenarios, highlighting user-adapted workflows and spontaneous layout strategies. Our findings suggest the potential of embedding mobile-inspired metaphors into proprioceptive body-centric interfaces to support low-effort and spatially coherent interaction in VR.

cs.HC↗

arXiv2025

What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulation

Biomechanical models allow for diverse simulations of user movements in interaction. Their performance depends critically on the careful design of reward functions, yet the interplay between reward components and emergent behaviours remains poorly understood. We investigate what makes a model "breathe" by systematically analysing the impact of rewarding effort minimisation, task completion, and target proximity on movement trajectories. Using a choice reaction task as a test-bed, we find that a combination of completion bonus and proximity incentives is essential for task success. Effort terms are optional, but can help avoid irregularities if scaled appropriately. Our work offers practical insights for HCI designers to create realistic simulations without needing deep reinforcement learning expertise, advancing the use of simulations as a powerful tool for interaction design and evaluation in HCI.

cs.HC↗

arXiv2025

AlphaPIG: The Nicest Way to Prolong Interactive Gestures in Extended Reality

Mid-air gestures serve as a common interaction modality across Extended Reality (XR) applications, enhancing engagement and ownership through intuitive body movements. However, prolonged arm movements induce shoulder fatigue, known as "Gorilla Arm Syndrome", degrading user experience and reducing interaction duration. Although existing ergonomic techniques derived from Fitts' law (such as reducing target distance, increasing target width, and modifying control-display gain) provide some fatigue mitigation, their implementation in XR applications remains challenging due to the complex balance between user engagement and physical exertion. We present AlphaPIG, a meta-technique designed to Prolong Interactive Gestures by leveraging real-time fatigue predictions. AlphaPIG assists designers in extending and improving XR interactions by enabling automated fatigue-based interventions. Through adjustment of intervention timing and intensity decay rate, designers can explore and control the trade-off between fatigue reduction and potential effects such as decreased body ownership. We validated AlphaPIG's effectiveness through a study (N=22) implementing the widely-used Go-Go technique. Results demonstrated that AlphaPIG significantly reduces shoulder fatigue compared to non-adaptive Go-Go, while maintaining comparable perceived body ownership and agency. Based on these findings, we discuss positive and negative perceptions of the intervention. By integrating real-time fatigue prediction with adaptive intervention mechanisms, AlphaPIG constitutes a critical first step towards creating fatigue-aware applications in XR.

cs.HC↗

arXiv2025

Optimizing Curve-Based Selection with On-Body Surfaces in Virtual Environments

Virtual Reality (VR) interfaces often rely on linear ray-casting for object selection but struggle with precision in dense or occluded environments. This late-breaking work introduces an optimized dual-layered selection mechanism combining dynamic Bezier Curves, controlled via finger gestures, with on-body interaction surfaces to enhance precision and immersion. Bezier Curves offer fine-grained control and flexibility in complex scenarios, while on-body surfaces project nearby virtual objects onto the user's forearm, leveraging proprioception and tactile feedback. A preliminary qualitative study ($N$ = 24) compared two interaction paradigms (Bezier Curve vs. Linear Ray) and two interaction media (On-body vs. Mid-air). Participants praised the Bezier Curve's ability to target occluded objects but noted the physical demand. On-body interactions were favored for their immersive qualities, while mid-air interactions were appreciated for maintaining focus on the virtual scene. These findings highlight the importance of balancing ease of learning and precise control when designing VR selection techniques, opening avenues for further exploration of curve-based and on-body interactions in dense virtual environments.

cs.HC↗

arXiv2025

EnVisionVR: A Scene Interpretation Tool for Visual Accessibility in Virtual Reality

Effective visual accessibility in Virtual Reality (VR) is crucial for Blind and Low Vision (BLV) users. However, designing visual accessibility systems is challenging due to the complexity of 3D VR environments and the need for techniques that can be easily retrofitted into existing applications. While prior work has studied how to enhance or translate visual information, the advancement of Vision Language Models (VLMs) provides an exciting opportunity to advance the scene interpretation capability of current systems. This paper presents EnVisionVR, an accessibility tool for VR scene interpretation. Through a formative study of usability barriers, we confirmed the lack of visual accessibility features as a key barrier for BLV users of VR content and applications. In response, we designed and developed EnVisionVR, a novel visual accessibility system leveraging a VLM, voice input and multimodal feedback for scene interpretation and virtual object interaction in VR. An evaluation with 12 BLV users demonstrated that EnVisionVR significantly improved their ability to locate virtual objects, effectively supporting scene understanding and object interaction.

cs.HC↗

arXiv2025

Human-inspired Perspectives: A Survey on AI Long-term Memory

With the rapid advancement of AI systems, their abilities to store, retrieve, and utilize information over the long term - referred to as long-term memory - have become increasingly significant. These capabilities are crucial for enhancing the performance of AI systems across a wide range of tasks. However, there is currently no comprehensive survey that systematically investigates AI's long-term memory capabilities, formulates a theoretical framework, and inspires the development of next-generation AI long-term memory systems. This paper begins by introducing the mechanisms of human long-term memory, then explores AI long-term memory mechanisms, establishing a mapping between the two. Based on the mapping relationships identified, we extend the current cognitive architectures and propose the Cognitive Architecture of Self-Adaptive Long-term Memory (SALM). SALM provides a theoretical framework for the practice of AI long-term memory and holds potential for guiding the creation of next-generation long-term memory driven AI systems. Finally, we delve into the future directions and application prospects of AI long-term memory.

cs.AI↗

arXiv2024

Generative AI for Accessible and Inclusive Extended Reality

Artificial Intelligence-Generated Content (AIGC) has the potential to transform how people build and interact with virtual environments. Within this paper, we discuss potential benefits but also challenges that AIGC has for the creation of inclusive and accessible virtual environments. Specifically, we touch upon the decreased need for 3D modeling expertise, benefits of symbolic-only as well as multimodal input, 3D content editing, and 3D model accessibility as well as foundation model-specific challenges.

cs.HC↗

arXiv2024

Large Language Model-assisted Speech and Pointing Benefits Multiple 3D Object Selection in Virtual Reality

Selection of occluded objects is a challenging problem in virtual reality, even more so if multiple objects are involved. With the advent of new artificial intelligence technologies, we explore the possibility of leveraging large language models to assist multi-object selection tasks in virtual reality via a multimodal speech and raycast interaction technique. We validate the findings in a comparative user study (n=24), where participants selected target objects in a virtual reality scene with different levels of scene perplexity. The performance metrics and user experience metrics are compared against a mini-map based occluded object selection technique that serves as the baseline. Results indicate that the introduced technique, AssistVR, outperforms the baseline technique when there are multiple target objects. Contrary to the common belief for speech interfaces, AssistVR was able to outperform the baseline even when the target objects were difficult to reference verbally. This work demonstrates the viability and interaction potential of an intelligent multimodal interactive system powered by large laguage models. Based on the results, we discuss the implications for design of future intelligent multimodal interactive systems in immersive environments.

cs.HC↗

arXiv2024

Swarm manipulation: An efficient and accurate technique for multi-object manipulation in virtual reality

The theory of swarm control shows promise for controlling multiple objects, however, scalability is hindered by cost constraints, such as hardware and infrastructure. Virtual Reality (VR) can overcome these limitations, but research on swarm interaction in VR is limited. This paper introduces a novel Swarm Manipulation interaction technique and compares it with two baseline techniques: Virtual Hand and Controller (ray-casting). We evaluated these techniques in a user study ($N$ = 12) in three tasks (selection, rotation, and resizing) across five conditions. Our results indicate that Swarm Manipulation yielded superior performance, with significantly faster speeds in most conditions across the three tasks. It notably reduced resizing size deviations but introduced a trade-off between speed and accuracy in the rotation task. Additionally, we conducted a follow-up user study ($N$ = 6) using Swarm Manipulation in two complex VR scenarios and obtained insights through semi-structured interviews, shedding light on optimized swarm control mechanisms and perceptual changes induced by this interaction paradigm. These results demonstrate the potential of the Swarm Manipulation technique to enhance the usability and user experience in VR compared to conventional manipulation techniques. In future studies, we aim to understand and improve swarm interaction via internal swarm particle cooperation.

cs.HC↗