Physiological Impact of Chromatic-Weight Illusions in Augmented Reality: A Comparative sEMG Analysis of Muscle Fatigue and Stability

During manual operations, the human brain relies on mediated visual stimuli such as color to estimate an object’s weight and adjust muscle force through the central nervous system (CNS). This study examines the neuromuscular “reality gap” induced by the color–weight illusion (CWI) during repetitive lifting tasks in an augmented reality (AR) interface. We analyzed the median frequency (MDF) and Co-Contraction Index (CCI) of the biceps and triceps muscles to quantify physiological strain under varying luminance conditions in both AR and physical environments. The results reveal that AR significantly amplifies the CWI, with black stimuli triggering an aggressive joint-stiffening strategy in the AR group (APG). Compared with the physical reality group, the AR group showed lower overall endurance (91.4 ± 22.8 vs. 100.1 ± 12.5 repetitions) and a stronger physiological response to the black stimulus. In the AR group, the black condition was associated with a terminal CCI of 84.7 ± 25.4% and an MDF decline of approximately 21.7 Hz, whereas the corresponding contrast was attenuated in the physical reality group. These findings demonstrate a critical decoupling between behavioral output and internal physiological strain, indicating that the CNS treats virtual visual cues as high-reliability signals that increase metabolic “bio-cost” despite task completion parity. This research identifies a “masking effect” where behavioral metrics hide severe ergonomic risks, providing novel approaches for managing musculoskeletal health in industrial settings and personalizing coordination training in clinical rehabilitation.

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Publication Details

Journal
Sensors
Published
2026-04-22
DOI
https://doi.org/10.3390/s26092575
Primary Topic
Motor Control and Adaptation
Type
article
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article

Physiological Impact of Chromatic-Weight Illusions in Augmented Reality: A Comparative sEMG Analysis of Muscle Fatigue and Stability

Guiqin Li, Julia Greenfield, Yi-Xiang Wang, Peter Mitrouchev et al.
Sensors
Motor Control and Adaptation
article

Physiological Impact of Chromatic-Weight Illusions in Augmented Reality: A Comparative sEMG Analysis of Muscle Fatigue and Stability

Guiqin Li, Julia Greenfield, Yi-Xiang Wang, Peter Mitrouchev, Franck Quaine
article en

Abstract

During manual operations, the human brain relies on mediated visual stimuli such as color to estimate an object’s weight and adjust muscle force through the central nervous system (CNS). This study examines the neuromuscular “reality gap” induced by the color–weight illusion (CWI) during repetitive lifting tasks in an augmented reality (AR) interface. We analyzed the median frequency (MDF) and Co-Contraction Index (CCI) of the biceps and triceps muscles to quantify physiological strain under varying luminance conditions in both AR and physical environments. The results reveal that AR significantly amplifies the CWI, with black stimuli triggering an aggressive joint-stiffening strategy in the AR group (APG). Compared with the physical reality group, the AR group showed lower overall endurance (91.4 ± 22.8 vs. 100.1 ± 12.5 repetitions) and a stronger physiological response to the black stimulus. In the AR group, the black condition was associated with a terminal CCI of 84.7 ± 25.4% and an MDF decline of approximately 21.7 Hz, whereas the corresponding contrast was attenuated in the physical reality group. These findings demonstrate a critical decoupling between behavioral output and internal physiological strain, indicating that the CNS treats virtual visual cues as high-reliability signals that increase metabolic “bio-cost” despite task completion parity. This research identifies a “masking effect” where behavioral metrics hide severe ergonomic risks, providing novel approaches for managing musculoskeletal health in industrial settings and personalizing coordination training in clinical rehabilitation.

SensorsVol. 26(9)
Institut polytechnique de Grenoble (FR), Shanghai University (CN), Centre National de la Recherche Scientifique (FR), Philipps University of Marburg (DE), Laboratoire des Sciences pour la Conception, l'Optimisation et la Production (FR), GIPSA-Lab (FR), Shanghai Jian Qiao University (CN), Université Grenoble Alpes (FR)
Openalex Percentile: Top 6%
Motor Control and Adaptation
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