  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; some of
which are derived from original videos: (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.
