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.
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