ArticleICMI'22 Companion : companion publication of the 2022 International Conference on Multimodal Interaction : November 7-11, 2022, Bangalore, India. ICMI (Conference) (2022 : Bangalore, India)2022
Head Movement Patterns during Face-to-Face Conversations Vary with Age.
Article in ICMI'22 Companion : companion publication of the 2022 International Conference on Multimodal Interaction : November 7-11, 2022, Bangalore, India. ICMI (Conference) (2022 : Bangalore, India), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A transdiagnostic AI-based measure of interpersonal coordination in autism and other conditions.Molecular autism · 2026Article
- Sex- and social context-dependent differences in mice fine head movement during social interactions.BMC biology · 2025Article
- Continuous Monitoring of Head Turns: Compliance, Kinematics, and Reliability of Wearable Sensing.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Predicting Autism from Head Movement Patterns during Naturalistic Social Interactions.Proceedings of the 2023 7th International Conference on Medical and Health Informatics (ICMHI 2023) : May 12-14, 2023, Kyoto, Japan. International Conference on Medical and Health Informatics (7th : 2023 : Kyoto, Japan) · 2023Article
- Comparison of Human Experts and AI in Predicting Autism from Facial Behavior.CEUR workshop proceedings · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
Advances in computational behavior analysis have the potential to increase our understanding of behavioral patterns and developmental trajectories in neurotypical individuals, as well as in individuals with mental health conditions marked by motor, social, and emotional difficulties. This study focuses on investigating how head movement patterns during face-to-face conversations vary with age from childhood through adulthood. We rely on computer vision techniques due to their suitability for analysis of social behaviors in naturalistic settings, since video data capture can be unobtrusively embedded within conversations between two social partners. The methods in this work include unsupervised learning for movement pattern clustering, and supervised classification and regression as a function of age. The results demonstrate that 3-minute video recordings of head movements during conversations show patterns that distinguish between participants that are younger vs. older than 12 years with 78% accuracy. Additionally, we extract relevant patterns of head movement upon which the age distinction was determined by our models.
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Registered trials
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