  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.
