The Eye-CU project incorporates a multiview aspect of the network to successfully remove the complex and prohibitively expensive pressure mat. This work uses purely visual rgb and depth sensors position at relatively different locations (i.e., multiview). Eye-CU learns the weights the contribution of each sensor and view via couple-constrained Least-Squares (cc-LS) modality trust estimation algorithm. The Eye-CU system in combination with cc-LS successfully match the performance of the MEYE network while and reliably classifies patient poses in challenging scene conditions (variable illumination and various sensor occlusions).