The inferred cost of work-related stress call for early prevention strategies. In this, we see a new opportunity for affective and pervasive computing by detecting early warning signs. This paper goes one step toward this goal. A collective of 33 subjects underwent a laboratory stress intervention, while a set of physiological signals was collected. In this paper, we investigate whether affective information related to stress can be found in the posture channel during office work. Following more recent work in this field, we directly associate features that are derived from the pressure distribution on a chair with affective states. We found that nervous subjects reveal higher variance of movements under stress. Furthermore, we show that a person-independent discrimination of stress from cognitive load is feasible when using pressure data only. A supervised variant of a self-organizing map, which is able to adapt to different patterns of stress responses, reaches an overall accuracy of 73.75% with unknown subjects.