1 recherche sur le mot-clé 'mannerisms'

Semi-Automated Multi-Label Classification of Autistic Mannerisms by Machine Learning on Post Hoc Skeletal Tracking / Solvejg K. KLEBER ; Leonie POLZER ; Naisan RAJI ; Janina KITZEROW-CLEVEN ; Ziyon KIM ; Simeon PLATTE ; Christine M. FREITAG ; Nico BAST in Autism Research, 18-4 (April 2025)  

Public ISBD [article]  inAutism Research > 18-4 (April 2025) . - p.833-844 Titre : Semi-Automated Multi-Label Classification of Autistic Mannerisms by Machine Learning on Post Hoc Skeletal Tracking Type de document : texte imprimé Auteurs : Solvejg K. KLEBER, Auteur ; Leonie POLZER, Auteur ; Naisan RAJI, Auteur ; Janina KITZEROW-CLEVEN, Auteur ; Ziyon KIM, Auteur ; Simeon PLATTE, Auteur ; Christine M. FREITAG, Auteur ; Nico BAST, Auteur Article en page(s) : p.833-844 Langues : Anglais (eng) Mots-clés : autism spectrum disorder  machine learning  mannerisms  multi-label classification  stereotypic behavior Index. décimale : PER Périodiques Résumé : ABSTRACT Mannerisms describe repetitive or unconventional body movements like arm flapping. These movements are early markers of restricted and repetitive behaviors (RRBs) in autism spectrum disorder (ASD). However, assessing mannerisms reliably is challenging. Even after extensive training in behavioral observations, inter-rater agreements for mannerism items remain insufficient. The current study used machine learning (ML) to classify mannerisms from videotaped behavioral observations in children with ASD. We developed a classification scheme for mannerisms as ground truth and applied it to videotaped behavioral observations from an early intervention study. ML was used in two steps: First, the OpenPose algorithm post hoc extracted features based on body movements in the videos. Second, a long short-term memory (LSTM) neural network classified the features in a multi-label approach to distinguish between the absence of mannerisms, flapping, jumping, and both flapping + jumping. The trained models achieved 70.2% accuracy (F1 score: 31.8%) using nested cross-validation. The analysis improves on previous videotaped ML classification studies by splitting training and test data subject-wise, highlighting its clinical applicability. The LSTM models are made publicly available for use with other video datasets. Our results show that ML-based classification of mannerisms is a promising tool for enhancing objective diagnostic methods of behavioral observations. En ligne : https://doi.org/10.1002/aur.70020 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=554 [article] Semi-Automated Multi-Label Classification of Autistic Mannerisms by Machine Learning on Post Hoc Skeletal Tracking [texte imprimé] / Solvejg K. KLEBER, Auteur ; Leonie POLZER, Auteur ; Naisan RAJI, Auteur ; Janina KITZEROW-CLEVEN, Auteur ; Ziyon KIM, Auteur ; Simeon PLATTE, Auteur ; Christine M. FREITAG, Auteur ; Nico BAST, Auteur . - p.833-844. Langues : Anglais (eng) in Autism Research > 18-4 (April 2025) . - p.833-844 Mots-clés : autism spectrum disorder  machine learning  mannerisms  multi-label classification  stereotypic behavior Index. décimale : PER Périodiques Résumé : ABSTRACT Mannerisms describe repetitive or unconventional body movements like arm flapping. These movements are early markers of restricted and repetitive behaviors (RRBs) in autism spectrum disorder (ASD). However, assessing mannerisms reliably is challenging. Even after extensive training in behavioral observations, inter-rater agreements for mannerism items remain insufficient. The current study used machine learning (ML) to classify mannerisms from videotaped behavioral observations in children with ASD. We developed a classification scheme for mannerisms as ground truth and applied it to videotaped behavioral observations from an early intervention study. ML was used in two steps: First, the OpenPose algorithm post hoc extracted features based on body movements in the videos. Second, a long short-term memory (LSTM) neural network classified the features in a multi-label approach to distinguish between the absence of mannerisms, flapping, jumping, and both flapping + jumping. The trained models achieved 70.2% accuracy (F1 score: 31.8%) using nested cross-validation. The analysis improves on previous videotaped ML classification studies by splitting training and test data subject-wise, highlighting its clinical applicability. The LSTM models are made publicly available for use with other video datasets. Our results show that ML-based classification of mannerisms is a promising tool for enhancing objective diagnostic methods of behavioral observations. En ligne : https://doi.org/10.1002/aur.70020 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=554