Influence on the user's emotional state of the graphic complexity level in virtual therapies based on a robot-assisted neuro-rehabilitation platform.

BACKGROUND

In recent years, a remarkable trend in neurorehabilitation is the combination of conventional methods and emerging technologies, such as robotic platforms with virtual reality (VR), Serious Games (SG) and other types of sophisticated graphic interfaces. The aim of the present study is to evaluate the influence on the user's mental workload of the degree of graphic detail present in this kind of environments, comparing the experience of subjects working with two graphical environments with the same physical load but radically different graphic detail levels.

METHODS

The same therapy is performed by 52 healthy subjects in two totally different graphic environments, one rich in details and visual stimuli, and its schematic version focusing just in the target graphic elements. Autonomic Nervous System (ANS) activity, related to emotional state, is analyzed through the capture and processing of associated physiological signals. The SAM test is used to assess the subjective perception of the participants in order to establish a relationship with the calculated physiological parameters.

RESULTS

Indexes calculated from the ANS physiological signals show significant differences between the basal state and those corresponding to the performance of the therapies but do not allow discriminating between the effects of performing a therapy based on an environment rich in stimuli and an austere one. In the case of subjective perceptions, based on the results of the SAM test, the subjects perceive significant differences between the basal stage and the therapies, and also between each of the two graphic environments.

CONCLUSIONS

Users reflected that a graphic environment rich in detail was more pleasant and attractive than a schematic one. However, this is not reflected in values obtained from their physiological activity, which suggests that more research is needed about the online inference of the emotional state of the subject from the record of his physiological activity.