Advanced modeling method for quantifying cumulative subjective fatigue in mid-air interaction. Issue 169 (January 2023)

• Record Type: Journal Article
• Authors: Villanueva, Ana; Jang, Sujin; Stuerzlinger, Wolfgang; Ambike, Satyajit; Ramani, Karthik
• Is Part Of: International journal of human-computer studies. Issue 169(2023)
• Publication Date: 2023-01
• Subjects: Mid-air interaction -- Cumulative fatigue model -- Maximum arm strength -- Brain effort; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2022.102931
• Languages: English
• ISSNs: 1071-5819

Abstract

Abstract: 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. Highlights: 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.