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

2025-03-02

AI Summary

Prolonged mid-air gestures in XR induce shoulder fatigue (“gorilla arm syndrome”), degrading user experience and interaction sustainability. Conventional Fitts’ law–based optimization methods fail to jointly mitigate fatigue and preserve sense of embodiment. This paper introduces AlphaPIG—the first fatigue-aware adaptive intervention framework for XR gesture interaction. It integrates a real-time fatigue prediction model combining electromyographic (EMG) and kinematic signals, dynamically modulates control-display gain, and incorporates an enhanced Go-Go interaction mechanism. Crucially, AlphaPIG supports tunable intervention timing and exponential decay rates for intervention intensity, enabling the first simultaneous optimization of fatigue suppression and both sense of ownership and agency. A user study (N=22) demonstrates statistically significant reduction in shoulder fatigue (p<0.01), with no significant degradation in either sense of ownership or agency. This work establishes a novel paradigm for sustainable, immersive, fatigue-aware XR systems.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Addresses shoulder fatigue in XR mid-air gestures
• Balances user engagement with physical exertion
• Enables fatigue-aware XR applications via real-time predictions

Innovation

• Real-time fatigue prediction for XR gestures
• Automated fatigue-based intervention adjustments
• Balancing fatigue reduction and user engagement