Autism spectrum disorder (ASD) is a developmental disorder characterized by significant social communication impairments and difficulties perceiving and presenting communication cues. Machine learning techniques have been broadly adopted to facilitate autism studies and assessments. However, computational models are primarily concentrated on specific analysis and validated on private datasets in the autism community, which limits comparisons across models due to privacy-preserving data sharing complications. This work presents a novel privacy-preserving open-source dataset, MMASD as a MultiModal ASD benchmark dataset, collected from play therapy interventions of children with Autism. MMASD includes data from 32 children with ASD, and 1,315 data samples segmented from over 100 hours of intervention recordings. To promote public access, each data sample consists of four privacy-preserving modalities of data: (1) optical flow, (2) 2D skeleton, (3) 3D skeleton, and (4) clinician ASD evaluation scores of children, e.g., ADOS scores. MMASD aims to assist researchers and therapists in understanding children's cognitive status, monitoring their progress during therapy, and customizing the treatment plan accordingly. It also has inspiration for downstream tasks such as action quality assessment and interpersonal synchrony estimation. MMASD dataset can be easily accessed at https://github.com/Li-Jicheng/MMASD-A-Multimodal-Dataset-for-Autism-Intervention-Analysis.

翻译：自闭症谱系障碍（ASD）是一种以显著社交沟通障碍及感知与呈现沟通线索困难为特征的发育性障碍。机器学习技术已被广泛应用于促进自闭症研究与评估，然而，计算模型主要集中于特定分析，并在自闭症领域内基于私有数据集进行验证，这因隐私保护性数据共享的复杂性而限制了不同模型间的比较。本研究提出一个新颖的隐私保护型开源数据集——MMASD，作为多模态ASD基准数据集，其数据采集自自闭症儿童的游戏治疗干预过程。MMASD包含32名自闭症儿童的数据，以及从超过100小时的干预记录中分割出的1,315个数据样本。为促进公共获取，每个数据样本包含四种隐私保护型数据模态：（1）光流、（2）二维骨骼、（3）三维骨骼，以及（4）临床医师对儿童的自闭症评估分数（例如ADOS评分）。MMASD旨在帮助研究人员和治疗师理解儿童的认知状态、监测治疗进展并据此定制治疗方案，同时为动作质量评估与人际同步估计等下游任务提供启发。MMASD数据集可通过https://github.com/Li-Jicheng/MMASD-A-Multimodal-Dataset-for-Autism-Intervention-Analysis 便捷获取。