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This study investigates the impact of different assistance strategies on human movement variability and user experience in smart wheelchair interactions. By comparing conventional variability-reducing assistance with an autonomy-supportive strategy that preserves natural movement structure, the research reveals that while task performance remains stable, the latter significantly enhances users' perceived agency and interaction satisfaction. These findings underscore the importance of recognizing and maintaining movement variability in the design of assistive robotic systems to improve user autonomy and experience.
Preserving natural movement variability in smart wheelchair interactions can significantly enhance user agency and satisfaction, challenging conventional assistance strategies.
Human movement is inherently variable, with variability structured according to task relevance: movements are typically more consistent at task-critical points and more flexible elsewhere. In human-robot interaction (HRI), however, model-based assistance strategies commonly assume deterministic human behavior and suppress such variability, potentially altering how interactions are experienced and lowering sense of agency. While movement variability is increasingly recognized as functionally meaningful, its deliberate preservation in assisted interaction, and its consequences for user experience, remain underexplored. In this paper, we empirically investigate how different assistance strategies shape human movement variability, task performance, and subjective interaction experience in a shared control setting. We introduce an autonomy-supportive shared control strategy that preserves users'natural movement structure. This approach is evaluated in a user study in which participants push an intelligent powered wheelchair under three conditions: no assistance, conventional variability-reducing assistance, and variability-preserving assistance. While task-relevant performance remained comparable across assisted modes, preserving natural movement variability led to more favorable interaction experiences. In particular, participants reported significantly higher perceived agency compared to conventional assistance and highest perceived usefulness. These findings suggest that variability-aware assistance can support both performance and user autonomy in physical human-robot collaboration. More broadly, the results highlight the importance of designing assistive robotic systems that respect the embodied structure of human movement rather than treating variability as noise to be neglected or eliminated.
