Biofeedback for robotic gait rehabilitation

Abstract

Background: Development and increasing acceptance of rehabilitation robots as well as advances in technology allow new forms of therapy for patients with neurological disorders. Robot-assisted gait therapy can increase the training duration and the intensity for the patients while reducing the physical strain for the therapist.

Evaluation of robot-assisted gait training using integrated biofeedback in neurologic disorders

“…High validity for using biofeedback has only been described for swing phase values [17], therefore stance phase values were excluded for data analysis. Subjects were analysed over 8 RAGT sessions.…”

“…Raw data were first anonymised and exported for every training session, as well as date of session, training duration, walking distance, patient coefficient (relation of treadmill speed and DGO-L speed), guidance force and walking speed using the biofeedback system from Lunenburger et al [16,17]. Biofeedback values, patient coefficient, guidance force, and walking speed were averaged for every session.…”

“…First validation on three subjects without neurological disorders showed high correlations during swing phase between positive biofeedback values and verbal instructions indicating that walking should be performed with effort, whereas body weight support and treadmill velocity had only minor effects on the biofeedback values [17]. Furthermore, the above-mentioned type of verbal instructions also resulted in significantly higher biofeedback values compared to passive walking using DGO-L in five subjects with SCI [18].…”

“…To address the problem of assessing the quality and quantity of patients movements, Riener and Lunenburger [16][17][18] developed a biofeedback system based on the integrated position encoders and force transducers which determine hip and knee joint torques in order to give direct information about the subject's activity walking activity in the predefined trajectories. The goal of this biofeedback system is to encourage the patient to improve therapeutically desirable activities in gait pattern such as active hip extension in stance phase to move the trunk forward, active knee extension to support the body weight during stance phase, active hip flexion in swing phase to pull the leg forward and active knee flexion followed by active knee extension in mid swing phase to bring the foot into initial contact position.…”

Estimating the patient�s contribution during robot-assisted therapy

“…In these applications, the score often depends only on the task performance and does not take the applied robotic assistance into account. Lünenburger et al proposed a method for the gait rehabilitation robot Lokomat, where the interaction forces between patient and exoskeleton are measured and multiplied by a weighting function to obtain the biofeedback metric about the patient's participation during one gait cycle [12]. The arm robot RUPERT calculates the amount of assistance by dividing the work measured during a movement with a reference work value, obtained from a passive arm movement [13].…”