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
this https URL

该研究提出了一种隐私保护的开源数据集 MMASD，它包括来自 32 个 ASD 儿童游戏治疗干预的数据，被分割成超过 100 个小时的干预录音，并由四个隐私保护数据模式组成，可帮助研究人员了解儿童的认知状态，监测他们在治疗期间的进展，并相应地定制治疗计划。