Individuals with severe neurologic injuries often cannot participate in robotic rehabilitation because they do not retain sufficient residual motor control to initiate the robotic assistance. In these situations, brain- and body-computer interfaces have emerged as promising solutions to control robotic devices. In a previous experiment conducted with healthy subjects, we showed that detecting motor execution accurately was possible using only the autonomic nervous system (ANS) response. In this paper, we investigate the feasibility of such a body–machine interface to detect motion intention by monitoring the ANS response in stroke survivors. Four physiological signals were measured (blood pressure, breathing rate, skin conductance response and heart rate) while participants executed and imagined a grasping task with their impaired hand. The physiological signals were then used to train a classifier based on hidden Markov models. We performed an experiment with four chronic stroke survivors to test the effectiveness of the classifier to detect rest, motor execution and motor imagery periods. We found that motion execution can be accurately classified based only on peripheral autonomic signals with an accuracy of 72.4%. The accuracy of classifying motion imagery was 62.4%. Therefore, attempting to move was a more effective strategy than imagining the movement. These results are encouraging to perform further research on the use of the ANS response in body-machine interfaces.