While word embeddings derive meaning from co-occurrence patterns, human language understanding is grounded in sensory and motor experience. We present SENSE (Sensorimotor Embedding Norm Scoring Engine), a learned projection model that predicts Lancaster sensorimotor norms from word lexical embeddings. We also conducted a behavioral study where 281 participants selected which among candidate nonce words evoked specific sensorimotor associations, finding statistically significant correlations between human selection rates and SENSE ratings across 6 of the 11 modalities. Sublexical analysis of these nonce word selection rates revealed systematic phonesthemic patterns for the interoceptive norm, suggesting a path towards computationally proposing candidate phonesthemes from text data.