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OTA-NN: Observational Therapy-Assistance Neural Network for Enhancing Autism Intervention Quality

Children with autism spectrum disorder (ASD) often require long-term intervention. Experienced therapists with better observation on children’s feedback and interaction could adjust their intervention strategies to achieve the responsiveness and dynamic adaptation. However, there is large unmet need of experienced therapists to provide high quality therapy. Most children’s intervention was run by less experienced therapists or parents lacking observation skills. This motivates us to provide some observation assistance for improving the effectiveness of intervention of ASD. In this paper, we present an Observational Therapy-Assistance Neural Network (OTA-NN) for bridging the observation ability gap between experienced therapists and unprepared therapists (include parents of children). In OTA-NN, an attention based neural network extracts features from children’s behavior data and a multiple instance learning (MIL) network predicts score for reflecting child’s training state during a therapy session. We adopt weakly-supervised learning to train our model. The experiments on DREAM dataset show our model can successfully distinguish children’s unpredictable behaviors under different training states.