Psychological State Estimation from Physiological Recordings during Robot-Assisted Gait Rehabilitation

Article excerpt


Treadmill training is an established treatment for gait rehabilitation in patients with neurological disorders, such as stroke, spinal cord injury (SCI), or traumatic brain injury [1-2]. To increase rehabilitation outcome in those patients, an increasing number of driven gait orthoses (DGOs) are available that automate gait training, such as the Lokomat (Hocoma AG; Volketswil, Switzerland), the Autoambulator (HealthSouth; Birmingham, Alabama), the LOPES (Lower-extremity Powered Exoskeleton, Laboratory Biomechanical Engineering, University of Twente; Enschede, the Netherlands), and the Gait Trainer (developed by Hesse et al. [4]) [3-7].

Active biomechanical engagement of patients in rehabilitation training has shown to be an important factor in successful rehabilitation results [8]. The patient's biomechanical effort can be quantified by torque and force sensors. This information is used to assess the patient's level of activity [9].

While many studies investigated biomechanical engagement, active mental engagement, which has also been shown to be a key factor in successful rehabilitation [10], cannot be assessed easily and has been previously neglected. The goal of our work is to determine if a patient is mentally engaged during the training in order to maximize motor learning during rehabilitation. From motor learning theory, it is known that the learning rate is maximal at a task difficulty level that positively challenges and excites subjects while not being too stressful or boring [11]. A task that is too easy for the subject will be perceived as boring and a task that is too difficult will overstress the subject, while an optimally challenging task should induce maximal mental engagement and optimal physical participation.

We developed an approach using psychophysiological signals to automatically estimate and classify a patient's psychological state, i.e., his or her mental engagement, during rehabilitation. We used measurements of heart rate (HR), breathing frequency, galvanic skin response (GSR), and skin temperature. To our best knowledge, estimation of psychophysiological states has never been performed, either during walking or in patients with neurological disorders. Using our new method, we process the psychological state data in real time. We introduce our method as a first step toward real-time, auto-adaptive gait training with management of subject engagement and potential to improve rehabilitation results by optimally challenging the subject at all times during exercise.


Active mental engagement has been shown to be a key factor for successful rehabilitation [10]. In our experiment, we have defined three different levels of mental engagement according to the circumplex model of affect [12] (Figure 1), in which emotions are defined by two dimensions: valence (ranging from unpleasant to pleasant) and arousal (ranging from deactivation to activation). We used virtual-reality (VR) environments during robot-assisted gait training to induce different levels of mental engagement in subjects. Challenging tasks in VR environments were shown to have a positive, motivating effect during rehabilitation [13]. In this context, boring, too stressful, or optimally challenging tasks can be the result of a VR task that is too easy, too difficult, or appropriate for the patient's abilities, respectively. A VR task that is too easy or underchallenging for the patient will be perceived as boring and a task that is too difficult will overstress the patient, while an optimally challenging task should excite and motivate the patient and cause maximal mental engagement and optimal physical participation.


In the present state of the art, mental engagement of patients is quantified by questionnaires--motivation, for example, can be quantified by the Intrinsic Motivation Inventory [14]. During gait rehabilitation, questionnaires are not appropriate for continuous, objective assessment of the psychological state of the patient. …


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