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

TL;DR

Gorilla Arm fatigue limits prolonged XR interactions. AlphaPIG proposes a general, fatigue-aware meta-technique that uses real-time fatigue predictions (via NICER) to adapt interaction parameters with two meta-parameters: a fatigue threshold $T_f$ and a decay rate $DR_\alpha$, enabling dynamic, non-disruptive interventions. In a Go-Go mid-air selection study (N=22), AlphaPIG reductions in cumulative fatigue were achieved while preserving body ownership and agency, with design guidance for timing and intensity of interventions. The work delivers an accessible Unity API and demonstrates a principled approach to fatigue-aware XR design, offering practical pathways to extend usable life of gestural interactions in VR/AR. It lays groundwork for broader fatigue-aware interaction systems across XR applications, including potential exergaming and rehabilitation contexts.

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.

Figures (9)

• Figure 1: Classification of physically ergonomic interaction techniques in XR by modalities and manipulation strategies. Methods within the blue dashed box are suitable applications of AlphaPIG.
• Figure 2: AlphaPIG provides two meta-parameters, $T_f$ and $DR_\alpha$, for testing different fatigue intervention effects. A higher fatigue threshold $T_f$ raises the fatigue level at which the interaction technique parameter $\theta$ is adjusted. The decay rate $DR_\alpha$ determines to what level a small increase in fatigue (above the threshold $T_f$) affects $\theta$. A small decay rate (e.g., $DR_\alpha$ = 0.1, green dotted line) results in a larger intervention effect for the same fatigue level $F$ than a medium ($DR_\alpha$ = 0.25, red dashed line) or large decay rate (such as $DR_\alpha$ = 0.45, purple loosely dotted line). The horizontal shift between lines indicates a delay in the intervention effect until the user exceeds the target fatigue: $T_f$=0 (blue solid line) triggers intervention immediately, $T_f$=5 (red dashed line) delays intervention until a higher fatigue level is reached, and $T_f$=10 (yellow dash-dotted line) further delays intervention until the user is even more fatigued.
• Figure 3: Left: Cumulative fatigue values grouped by Timing (x-axis) and Decay Speed (color-coded), with baseline conditions Default and Go-Go included for comparison (shown in green and purple boxes, respectively). Right: Mean cumulative fatigue values across all participants, plotted by Timing (x-axis) and Decay Speed (y-axis).
• Figure 4: Left: TCT grouped by Timing (x-axis) and Decay Speed (color-coded), with baseline conditions Default and Go-Go included for comparison (shown in green and purple boxes, respectively). Right: Mean TCT across all participants, plotted by Timing (x-axis) and Decay Speed (y-axis).
• Figure 5: Questionnaire responses (Q1-Q4) grouped by Timing (x-axis) and Decay Speed (color-coded), with baseline conditions Default and Go-Go included for comparison (shown in green and purple boxes, respectively).