A novel implicit communication framework in human-machine interaction that is sensitive to human affective states is presented in this paper. The focus is to achieve detection and recognition of human affect based on physiological signals. This involves building an affect recognition system that accepts as input various physiological parameters and predicts the probable related affective state. Both decision tree and fuzzy logic methodologies have been applied to this problem. This paper presents the results of the two methods and discusses their comparative merit. Three human subject experiments were designed and trials were conducted with six participants. The experimental results demonstrate the feasibility of the proposed implicit human-machine interaction framework.