Non-invasive, non-contact based affective state identification

This paper discusses a study on detecting affective states of human subjects from their body’s electromagnetic (EM) wave. In particular, the affective states under investigation are happy, nervous, and sad which play important roles in Human-Robot Interaction (HRI)applications. A structured experimental setup was designed to invoke the desired affective states. These states are induced by exposing the subject to a specific set of audiovisual stimulations upon which the EM waves are captured from ten different regions of the subject’s body by using a handheld device called Resonant Field Imaging (RFITM). Nine subjects are randomly chosen and the collected data are then preprocessed and trained by Bayesian Network (BN) to map the EM wave to the corresponding affective states. Preliminary results demonstrate the ability of the BN to predict human affective state with 80.6% precision, and 90% accuracy

Emotion embodiment in robot-assisted rehabilitation system using hybrid automata

The embodiment of emotions in the paper is structured under hybrid automata framework. In particular, the paper focuses on the description of the automata model designed for robot-assisted rehabilitation system in term of its initialization value, modes, condition for each mode, guard conditions, and transition between modes. A structured experimental setup was designed to evaluate the performance of the hybrid automata proposed. The result demonstrates the efficacy of hybrid automata approach in the rehabilitation application where emotion of the subject is taken into consideration in deploying suitable rehabilitation tasks