Sujin Jang, Wolfgang Stuerzlinger, Satyajit Ambike, Karthik Ramani
Modeling Cumulative Arm Fatigue in Mid-Air Interaction based on Perceived Exertion and Kinetics of Arm Motion
In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), Denver, CO, May 6-11, 2017 (Acceptance Rate: 25%)
CumulativeFatigue_CHI17

Estimating subjective fatigue based on cumulative fatigue model and biomechanical upper limb analysis in mid-air interaction. Left: sketches of mid-air pointing tasks performed in the experiments. Middle: biomechanical model of the upper limb and three-compartment muscle (TCM) model. Right: results of the leave-one-out cross-validation of the TCM model across all subject data (green-upward/downward triangles: upper/lower bound of ground-truth, blue circles: averaged ground-truth, black crosses: TCM estimates, red circles: averaged TCM estimages, orange/purple circles: averaged existing fatigue metric).

Abstract: Quantifying cumulative arm muscle fatigue is a critical factor in understanding, evaluating, and optimizing user experience during prolonged mid-air interaction. A reasonably accurate estimation of fatigue requires an estimate of an individual’s strength. However, there is no easy-to-access method to measure individual strength to accommodate inter-individual differences. Furthermore, fatigue is influenced by both psychological and physiological factors, but no current HCI model provides good estimates of cumulative subjective fatigue. We present a new, simple method to estimate the maximum shoulder torque through a mid-air pointing task, which agrees with direct strength measurements. We then introduce a cumulative fatigue model informed by subjective and biomechanical measures. We evaluate the performance of the model in estimating cumulative subjective fatigue in mid-air interaction by performing multiple cross-validations and a comparison with an existing fatigue metric. Finally, we discuss the potential of our approach for real-time evaluation of subjective fatigue as well as future challenges.

Downloads:
We will release the fatigue model implementation and the biomechanical upper limb analysis [download].

Sujin
About Sujin Jang

Sujin Jang is a PhD candidate in the School of Mechanical Engineering at Purdue University. He received B.S. in Mechanical and Automotive Engineering from Kookmin University, South Korea (2010) and completed M.S. in Mechanical Engineering at the University of Florida (2012). He is currently working at the C-Design Lab as a research assistant under advisory of Dr. Ramani. He has research experience in vision-based estimation and its robotic applications, machine learning, and computer vision. His current research broadly involves human-computer interactions, visual analytics, and machine learning. In particular, the goal of his research aims (i) to create natural human-computer interaction systems using human body as an interaction modality, and (ii) to establish the fundamental principles when designing such novel interaction systems. [Personal Website]

Publications
Posted in 2017, Karthik Ramani, Mid-air Interaction, Publications, Recent Publications, Spatial Analytics, Sujin Jang