Advanced modeling method for quantifying cumulative subjective fatigue in mid-air interaction.

Interaction in mid-air can be fatiguing. A model-based method to quantify cumulative subjective fatigue for such interaction was recently introduced in HCI research. This model separates muscle units into three states: active (M A) fatigued (M F) or rested (M R) and defines transition rules between states. This method demonstrated promising accuracy in predicting subjective fatigue accumulated in mid-air pointing tasks. In this paper, we introduce an improved model that additionally captures the variations of the maximum arm strength based on arm postures and adds linearly-varying model parameters based on current muscle strength. To validate the applicability and capabilities of the new model, we tested its performance in various mid-air interaction conditions, including mid-air pointing/docking tasks, with shorter and longer rest and task periods, and a long-term evaluation with individual participants. We present results from multiple cross-validations and comparisons against the previous model and identify that our new model predicts fatigue more accurately. Our modeling approach showed a 42.5% reduction in fatigue estimation error when the longitudinal experiment data is used for an individual participant's fatigue. Finally, we discuss the applicability and capabilities of our new approach. • Posture-based maximum strength representation is compatible with the TCM fatigue modeling method. • Subjective fatigue and muscle fatigue can be connected without contact-based measurement. • A reliable cumulative fatigue model is introduced based on brain effort (BE). • Designers can run our model in Kinect-based systems with a user's joint torque and Borg ratings. • A 42.5% reduction in fatigue estimation error for individualized modeling. [ABSTRACT FROM AUTHOR]