The objective of this research is to develop methods and tools for a multimodal and multi-sensor assessment and rehabilitation game system called CPLAY for children with Cerebral Palsy (CP). CPLAY collects and processes multiple types of stimulation and performance data while a child is playing. Its core has a touch-screen programmable game that has various metrics to measure delay of response, score, stamina/duration, accuracy of motor/hand motion. Optional devices attached to extend CPLAY versions provide additional parallel measurements of level of concentration/participation/engagement that quantify rehabilitation activity. The approach is to model the process as a cyber-physical system (CPS) feedback loop whereby data collected from various physical 3D devices (including fNIR brain imaging) are processed into hierarchical events of low-to-high semantic meaning that impact/ adjust treatment decisions. Intellectual Merit: The project will produce groundbreaking algorithms for event identification with a multi-level data to knowledge feedback loop approach. New machine learning, computer vision, data mining, multimodal data fusion, device integration and event-driven algorithms will lead towards a new type of cyber- physical rehabilitation science for neurological disorders. It will deliver fundamental advancements to engineering by showing how to integrate physical devices with a computationally quantitative platform for motor and cognitive skills assessment. Broader Impacts: The project delivers a modular & expandable game system that has huge implications on the future of US healthcare and rehabilitation of chronic neurological disabilities. It brings hope to children with Cerebral Palsy via lower cost and remote rehabilitation alternatives. It brings new directions to human centered computing for intelligent decision-making that supplements evidence-based practices and addresses social and psychological isolation problems.