Expanding the Integration of Multiscopic Cyber-Physical-Social System with Physical Sensors in Block-Design Test

This paper presents an expansion of the integrated multiscopic Cyber-Physical-Social System (CPSS), designed to assess physical and cognitive aspects within an independent Block-Design Test (BDT). Our approach comprises three tiers. At the microscopic level, we utilize a physical sensor to capture physical features during hand manipulation. Moving to the mesoscopic level, we incorporate block rotation and represent them in a graph data structure. At the macroscopic level, an upper table vision system employs color feature recognition within each block. Subsequently, Graph Convolutional Networks (GCN) represent the graph generated at the mesoscopic level in an embedding space. Our system effectively quantifies physical and cognitive variables during BDT exercises by evaluating eight WAIS-IV BDT designs. While our findings are promising, further research and development are essential to advance BDT applications.