InnoVista

Early detection of Autism Spectrum Disorder (ASD) in young children remains a significant challenge, especially in resource-limited settings, due to reliance on subjective clinical assessments, shortage of trained specialists, and absence of continuous monitoring systems. As a result, many children are diagnosed late, delaying critical early interventions. There is a need for an affordable, real-time, and objective monitoring system that can track physiological signals (heart rate, SpO₂) and behavioral patterns (repetitive movements) outside clinical environments. The challenge is to develop a wearable, sensor-based solution integrated with machine learning that can analyze multimodal data and provide early indicators of ASD, enabling timely intervention and accessible support for caregivers and healthcare providers.