Understanding the Influence of Fatigue on Full Arm Gestures in Augmented Reality Environments

Abstract

This research investigates fatigue’s impact on arm gestures within augmented reality environments. Through the analysis of the gathered data, our goal is to develop a comprehensive understanding of the constraints and unique characteristics affecting the performance of arm gestures when individuals are fatigued. Based on our findings, prolonged engagement in full-arm movement gestures under the influence of fatigue resulted in a decline in muscle strength within upper body segments. Thus, this decline led to a notable reduction in the accuracy of gesture detection in the AR environment, dropping from an initial 97.7% to 75.9%. We also found that changes in torso movements can have a ripple effect on the upper and forearm regions. This valuable knowledge will enable us to enhance our gesture detection algorithms, thereby enhancing their precision and accuracy, even in fatigue-related situations.