PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1928
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Conference Object Citation - WoS: 3Citation - Scopus: 3Detecting Autism From Head Movements Using Kinesics(Assoc Computing Machinery, 2024-11-04) Gokmen, Muhittin; Sariyanidi, Evangelos; Yankowitz, Lisa; Zampella, Casey J.; Schultz, Robert T.; Tunc, BirkanHead movements play a crucial role in social interactions. The quantification of communicative movements such as nodding, shaking, orienting, and backchanneling is significant in behavioral and mental health research. However, automated localization of such head movements within videos remains challenging in computer vision due to their arbitrary start and end times, durations, and frequencies. In this work, we introduce a novel and efficient coding system for head movements, grounded in Birdwhistell's kinesics theory, to automatically identify basic head motion units such as nodding and shaking. Our approach first defines the smallest unit of head movement, termed kine, based on the anatomical constraints of the neck and head. We then quantify the location, magnitude, and duration of kines within each angular component of head movement. Through defining possible combinations of identified kines, we define a higher-level construct, kineme, which corresponds to basic head motion units such as nodding and shaking. We validate the proposed framework by predicting autism spectrum disorder (ASD) diagnosis from video recordings of interacting partners. We show that the multi-scale property of the proposed framework provides a significant advantage, as collapsing behavior across temporal scales reduces performance consistently. Finally, we incorporate another fundamental behavioral modality, namely speech, and show that distinguishing between speaking- and listening-time head movements significantly improves ASD classification performance.Article Citation - WoS: 11Citation - Scopus: 13Social Competence in Children With Autism(Taylor & Francis, 2017-07-04) Yavuz, H. Melis; Selçuk, Bilge; Korkmaz, BarışObjectives: This paper investigates the associations of social competence with cognitive representation and communication skills in children with Autism Spectrum Disorders (ASD), by measuring these skills in an expansive way through assessing both mental and internal-state understanding, and verbal and non-verbal communication. Methods: The data were collected from 45 Turkish children (Mage=8.52 years, SD=3.05, min-max=3–14) with a diagnosis of ASD. Individual assessments were used to measure mental- and internal-state understanding. Teacher-rated scales were used to assess child’s verbal and non-verbal communication skills, and social competence. Results: The results showed that social competence, cognitive representation, verbal and non-verbal communication skills were all significantly associated, but over and above cognitive representation skills and verbal communication, non-verbal communication had a salient role in adaptive social relationships of children with ASD. Conclusions: These findings have important applied implications for intervention studies and suggest that improvements of non-verbal communication skills in children with ASD might be important for increasing their positive social relations.