Transformer-Based Physiological Emotion Recognition for Autism Intervention Support

Emotion recognition from wearable physiology could enable timely, personalized support when behavioural cues are atypical or self-report is limited. This need is particularly acute for students with Autism Spectrum Disorder (ASD) during Applied Behavior Analysis (ABA) therapy, where therapists must often infer internal states indirectly and in real time. Although Transformer architectures excel at modelling complex temporal dependencies, most physiological emotion-recognition models are developed on neurotypical cohorts and are rarely evaluated under therapy-relevant validation protocols or integrated into operational ABA workflows. Here, we show that a supervised Transformer operating on continuous 1D peripheral physiological time series supports practical emotion recognition for ASD intervention and is associated with measurable improvements in therapy. Using therapist annotations strictly as training targets (not as input features), we benchmarked the model on an expert-curated dataset of 1,459,337 therapist-labelled samples from$n=12$students with ASD collected during authentic sessions, achieving Leave-One-Session-Out (LOSecO) subject-specific accuracies of 84.28% (valence), 85.73% (arousal), and 80.26% (5-class emotion). In an independent user-study cohort ($N=33$, 1,722 sessions), integrating the system into ABA sessions was associated with statistically significant improvements in students' performance compared with standard practice. The system is further deployed in a production ABA platform serving over 2,000 students across 88 sites in Hong Kong, Singapore, and Canada, with 18 ms per-window inference latency and 99.97% uptime over three months. Our results demonstrate that Transformer-based physiological emotion recognition can be implemented reliably within the constraints of large-scale special education platforms, supporting practical translation from algorithmic performance to real-world intervention settings.