NICER: A New and Improved Consumed Endurance and Recovery Metric to Quantify Muscle Fatigue of Mid-Air Interactions

TL;DR

NICER addresses the challenge of predicting muscle fatigue in mid-air interactions by integrating a torque-based exertion measure with a correction term derived from EMG-driven muscle contraction, plus a recovery factor for breaks. The approach replaces or augments prior models (CE, CF, NICE) with a shoulder-specific endurance curve, a gesture-based maximum strength estimate via Chaffin's model, and a recovery mechanism, yielding a ready-to-use analytical tool applicable to unconstrained arm gestures. Across three studies, NICER demonstrates superior differentiation between high- and low-fatigue interaction methods, strong correlation with Borg CR10, and explicit recovery dynamics during breaks, while highlighting areas for further calibration and generalization to horizontal plane and bimanual tasks. The work offers practical implications for gesture-based interface design, enabling fatigue-aware timing, posture optimization, and adaptive interaction strategies in VR/AR/MR and related mid-air systems.

Abstract

Natural gestures are crucial for mid-air interaction, but predicting and managing muscle fatigue is challenging. Existing torque-based models are limited in their ability to model above-shoulder interactions and to account for fatigue recovery. We introduce a new hybrid model, NICER, which combines a torque-based approach with a new term derived from the empirical measurement of muscle contraction and a recovery factor to account for decreasing fatigue during rest. We evaluated NICER in a mid-air selection task using two interaction methods with different degrees of perceived fatigue. Results show that NICER can accurately model above-shoulder interactions as well as reflect fatigue recovery during rest periods. Moreover, both interaction methods show a stronger correlation with subjective fatigue measurement (r = 0.978/0.976) than a previous model, Cumulative Fatigue (r = 0.966/ 0.923), confirming that NICER is a powerful analytical tool to predict fatigue across a variety of gesture-based interactive applications.

Figures (11)

• Figure 1: Fatigue modelling pipeline for mid-air interaction, requires differentiating two key measures: exertion and fatigue.
• Figure 2: Left: The ET function implemented in CE (orange), NICE (blue), and NICER (green). Right: The instantaneous exertion measured in shoulder torque (purple) and muscle contraction (pink) under different shoulder angles.
• Figure 3: A summary of three studies conducted in the current paper.
• Figure 4: Mean values of Borg_Slope (left) and Duration (right) under each Shoulder_Angle in Study 1. Bars represent $\pm 1$ SE.
• Figure 5: Mean values of TR_Slope (top left), MD_Slope (top right), AD_Slope (bottom left), and IF_Slope (bottom right) under each Shoulder_Angle in Study 1. Bars represent $\pm 1$ SE.