Quantifying Body Motion Synchrony in Autism Spectrum Disorder Using a Phase Difference Detection Algorithm: Toward a Novel Behavioral Biomarker

利用相位差检测算法量化自闭症谱系障碍患者的身体运动同步性:迈向新型行为生物标志物

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Abstract

Background/Objectives: Nonverbal synchrony-the temporal coordination of physical behaviors such as head movement and gesture-is a critical component of effective social communication. Individuals with autism spectrum disorder (ASD) are often described as having impairments in such synchrony, but objective and scalable tools to measure these disruptions remain limited. This study aims to assess body motion synchrony in ASD using phase-based features as potential markers of social timing impairments. Methods: We applied a phase difference detection algorithm to high-resolution triaxial accelerometer data obtained during structured, unidirectional verbal communication. A total of 72 participants (36 typically developing TD-TD and 36 TD-ASD) were divided into dyads. ASD participants always assumed the listener role, enabling the isolation of receptive synchrony. Four distribution-based features-synchrony activity, directionality, variability, and coherence-were extracted from the phase difference data to assess synchrony dynamics. Results: Compared to the TD group, the ASD group exhibited significantly lower synchrony activity (ASD: 5.96 vs. TD: 9.63 times/min, p = 0.0008, Cohen's d = 1.23), greater temporal variability (ASD: 384.4 ms vs. TD: 311.1 ms, p = 0.0036, d = 1.04), and reduced coherence (ASD: 0.13 vs. TD: 0.81, p = 0.036, d = 0.73). Although the mean phase difference did not differ significantly between groups, the ASD group displayed weaker and more irregular synchrony patterns, indicating impaired temporal stability. Conclusions: Our findings highlight robust impairments in nonverbal head motion synchrony in ASD, not only in frequency but also in terms of temporal stability and convergence. The use of phase-based synchrony features provides a continuous, high-resolution, language-independent metric for social timing. These metrics offer substantial potential as behavioral biomarkers for diagnostic support and intervention monitoring in ASD.

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