Article

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.

Langue : ANGLAIS