Journal of Rehabilitation Research & Development (JRRD)

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

Alexander Koenig, MSc, et al.

Figure 7. First two activation coefficients of the principal component analysis (PCA) exemplarily shown for one patient with neurological disorders (patient 3), separated for conditions “baseline,” “underchallenged,” “challenged,” and “overchallenged,” plotted for whole length of each condi-tion (5 minutes). Within one color, darkness of color symbolizes later time during condition. While PCA was performed at once on whole data set, colors illustrate temporal evolution of data over conditions. PC = principal component.

We describe a method to objectively determine if robot-assisted gait training optimally challenges a patient. This information is important because patients benefit most from rehabilitation training that positively challenges and motivates them while not being too stressful or boring. We used physiological recordings to automatically classify whether the patient is mentally engaged. While nondisabled subjects could report this information verbally, cerebral lesions caused by traumatic brain injury or stroke might limit other patients' communication or self-assessment capabilities. Our method is the first approach toward a gait-training environment that detects the mental state of a patient and automatically adapts the training to the patient's needs and demands.

Volume 48 Number 4, 2011
   Pages 367 — 386

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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