Abstract— Efficacy of virtual rehabilitation applications is

typically demonstrated by pre and post comparisons of

observable behavioral metrics. These behaviors can be

monitored via devices such as trackers or video capture and

more traditional error rate metrics. However, monitoring the

patient’s emotional and cognitive changes during virtual

rehabilitation may better guide the rehab process as well as the

design of the rehab scenario. We explored the use of

biophysiological metrics (EEG, GSR, and Respiration) in the

design of a virtual restaurant for the purpose of engaging

persons who stutter in verbal interactions during an everyday

experience. The EEG results showed that participants

experienced higher engagement in the virtual restaurant.

Although respiration and GSR metrics differed for each

participant, they correlated well with stressors presented in the

scenario. The work supports the use of biophysiological

measures as an objective means of assessing virtual

rehabilitation protocols.

I. INTRODUCTION

irtual rehabilitation has many advantages as a rehabilitation tool [1]; however understanding

outcomes of such protocols is difficult [2]. Thus, we explored the use of unobtrusive, wearable, wireless biophysiological sensors (i.e., B-Alert EEG and Procomp Infiniti GSR and Respiration) to assess the level of anxiety and engagement elicited from persons who stutter as they interacted in everyday conversation within a virtual restaurant scenario created using the Mixed Reality Software Suite [3]. The goal of the pilot study was to determine if a virtual setting was more engaging than traditional role play in creating stressors that elicit stuttering responses in patients enrolled in a therapy program.

II. RESULTS

Two participants seeking treatment for stuttering at the Communicative Disorders Clinic at the University of Central Florida participated in the study. EEG, GSR, and Respiration recordings were taken during a pre and post baseline task as well as during the virtual restaurant interaction Figure 1 shows the preliminary engagement results as classified by the B-Alert EEG system [4]. GSR and Respiration measures were correlated with stressors

Manuscript received April 15, 2007. This work was supported by an In-House Grant given to the authors by the Institute for Simulation and Training at the University of Central Florida. C. M. Fidopiastis is with the Institute for Simulation and Training at the University of Central Florida, Orlando, FL 32826 USA (phone: 407-882-1451; fax: 407-882-1335; e-mail: cfidopia@ist.ucf.edu).

experienced (e.g., being rushed) during the virtual scenario; however, these measures showed individual differences.

III. CONCLUSION

The inclusion of biophysiological sensing devices allows us to further evaluate the efficacy of the VE based rehabilitation training scenario. More importantly, these devices may provide more instructive information on how the VE rehabilitation experience changes the participants’ cognitive function as well emotional state.

ACKNOWLEDGMENT

Thanks go to Jeff Wirth and the Interactive Performance Lab for their assistance with the scenario, members of the ACTIVE Lab for their expertise in sensor setup, and Dr. Martine Vanryckeghem for providing participants and the clinical application.

REFERENCES

[1] A. A. Rizzo, et al. “Synthetic Analysis of assets for virtual reality applications in neuropsychology,” Neuropsych Rehab, 14(1/2): 207–239, 2004.

[2] C. M. Fidopiastis, et al. “Human experience modeler: Context-driven cognitive retraining to facilitate transfer of learning,” CyberPsych &

Behav, 9(2): 183–187, 2006. [3] C. E. Hughes, et al., Emerging Technologies of Augmented Reality:

Interfaces and Design. Hershey, PA: Idea Group, Inc., 2006, 198–216. [4] C. Berka, et al. “Synthetic Real-time Analysis of EEG Indices of

Alertness, Cognition, and Memory with a Wireless EEG Headset,” Int

J Hum Comput Interact, 17(2): 151–170, 2004.

Assessing Virtual Rehabilitation Design with Biophysiological

Metrics

Cali M. Fidopiastis Member IEEE, Charles E. Hughes Senior Member IEEE, Eileen M. Smith and Denise M. Nicholson

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Fig. 1. Pre, virtual experience, and post %High Engagement for both participants as measured by the B-Alert EEG.

861-4244-1204-8/07/$25.00 ©2007 IEEE