Structural hidden markov model and its application in automotive industry

Abstract

We have introduced a new methodology that maps designs to human perceptions. Perceptions are adjectives/ adverbs, phrases or sentences expressed in natural language. We used the lexical database WordNet to compute semantical relationships (or distances) between these perceptions. We partitioned the set of perceptions into k clusters that represent the classes for a further classification task. We have developed a new classifier called .structural hidden Markov model. (SHMM) that combines probability and distances in a seamless way. SHMM enables to learn and predict user perceptions given object designs. We have applied this approach to Kansei engineering in order to map car external contours (shapes) to customer perceptions. The accuracy obtained using the SHMM is 90%. This model has outperformed the neural network and the k-nearest-neighbor classifiers.