Journal of Rehabilitation Research & Development (JRRD)

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Volume 48 Number 4, 2011
   Pages 367 — 386

Abstract —  Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation

Alexander Koenig, MSc;1-2* Ximena Omlin, MSc;1 Lukas Zimmerli, MSc;1,3 Mark Sapa, MD;4 Carmen Krewer, MSc;4 Marc Bolliger, PhD;1-2 Friedemann Müller, MD, MSc;4 Robert Riener, PhD1-2

1Sensory-Motor Systems Laboratory, Department of Mechanical and Process Engineering, Eidenössische Technische Hochschule Zurich, Zurich, Switzerland; 2Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; 3Hocoma AG , Volketswil, Switzerland; 4Neurologische Klinik Bad Aibling, Bad Aibling, Germany

Abstract — Robot-assisted treadmill training is an established intervention used to improve walking ability in patients with neurological disorders. Although it has been shown that attention to the task is a key factor for successful rehabilitation, the psychological state of patients during robot-assisted gait therapy is often neglected. We presented 17 nondisabled subjects and 10 patients with neurological disorders a virtual-reality task with varying difficulty levels to induce feelings of being bored, excited, and overstressed. We developed an approach to automatically estimate and classify a patient's psychological state, i.e., his or her mental engagement, in real time during gait training. We used psychophysiological measurements to obtain an objective measure of the current psychological state. Automatic classification was performed by a neural network. We found that heart rate, skin conductance responses, and skin temperature can be used as markers for psychological states in the presence of physical effort induced by walking. The classifier achieved a classification error of 1.4% for nondisabled subjects and 2.1% for patients with neurological disorders. Using our new method, we processed the psychological state data in real time. Our method is a first step toward real-time auto-adaptive gait training with potential to improve rehabilitation results by optimally challenging patients at all times during exercise.

Key words: automatic classification, gait robot, Lokomat, mental engagement, physiological measurements, psychophysiology, rehabilitation, spinal cord injury, stroke, virtual reality.

View HTML  ¦  View PDF  ¦  Contents Vol. 48, No. 4
This article and any supplementary material should be cited as follows:
Koenig A, Omlin X, Zimmerli L, Sapa M, Krewer C, Bolliger M, Müller F, Riener R. Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation. J Rehabil Res Dev. 2011; 48(4):367-86.

Last Reviewed or Updated  Tuesday, May 10, 2011 1:39 PM

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