Participant identification in haptic systems using hidden Markov models

Biometric systems allow identification of individuals based on behavioral or physiological characteristics. We explore biometric applications in access control of haptic systems. These systems produce information on human computer interaction behavior of a specific participant and could potentially be unique. This work proposes a novel design based on hidden Markov models (HMM). Architecture is developed where each participant has an HMM model. Results are promising in that they show three out of four users identified correctly from their respective models based on the match score (MS) values.