Human Gesture and Gait Analysis for Autism Detection

Autism diagnosis presents a major challenge due to the vast heterogeneity of the condition and the elusive nature of early detection. Atypical gait and gesture patterns are dominant behavioral characteristics of autism and can provide crucial insights for diagnosis. Furthermore, these data can be collected efficiently in a non-intrusive way, facilitating early intervention to optimize positive outcomes. Existing research mainly focuses on associating facial and eye-gaze features with autism. However, very few studies have investigated movement and gesture patterns which can reveal subtle variations and characteristics that are specific to autism. To address this gap, we present an analysis of gesture and gait activity in videos to identify children with autism and quantify the severity of their condition by regressing autism diagnostic observation schedule scores. Our proposed architecture addresses two key factors: (1) an effective feature representation to manifest irregular gesture patterns and (2) a two-stream co-learning framework to enable a comprehensive understanding of its relation to autism from diverse perspectives without explicitly using additional data modality. Experimental results demonstrate the efficacy of utilizing gesture and gait-activity videos for autism analysis.