Evaluation of robot-assisted gait training using integrated biofeedback in neurologic disorders Oliver Stoller a, *, Marco Waser b , Lukas Stammler a , Corina Schuster c a Bern University of Applied Sciences, Health, Basel, Switzerland b Swiss Tropical and Public Health Institute (TPH), Basel, Switzerland c Reha Rheinfelden, Rheinfelden, Switzerland 1. Introduction One of the most important skills necessary to be independent in daily life is the ability to walk. This skill is frequently reduced in people with neurological disorders. Over the last 20 years, robotic devices have been developed to support neurological rehabilita- tion and have become very popular in the field of gait training. Due to numerous potential advantages over conventional rehabilita- tion strategies [1], robot-assisted gait training (RAGT) can deliver time unlimited training sessions and needs less human resources than therapist-assisted gait training. Furthermore, several param- eters of leg kinematics can be controlled accurately allowing the objective quantification of walking performance and recovery [2]. One of the worldwide established robot-assisted driven gait- orthosis (DGO-L) is the Lokomat (Hocoma AG, Volketswil, Switzerland), which consists of a treadmill, a dynamic unloading system to relieve body weight, and two lightweight robotic actuators, which are attached to the subject’s legs. The hip and knee joints are actuated in the sagital plane by linear drives, which are integrated in an exoskeletal structure. The kinematic trajecto- ries of the DGO-L are fully programmable, and are adjusted to each individual’s size and step preference [3]. Previous studies suggested promising effects in walking ability, endurance and lower extremity function in patients with different neurological disorders such as stroke [4–7], spinal cord injuries (SCI) [8–11], traumatic brain injuries (TBI) [12], multiple sclerosis (MS) [13], and cerebral palsy (CP) in childhood and adolescence [14,15]. A recent review on electromechanical-assisted gait training for walking after stroke has shown significant increases in walking ability and walking capacity [7]. Nevertheless, training efficacy depends on a number of different parameters such as training dosing (initiation, duration, frequency) and training conditions (walking speed, guidance force, body weight support) that need to be examined in detail to improve therapeutic interventions and fully explore its effects. Until now, there is a lack of evaluation of specific parameters during RAGT on DGO-L. Furthermore, to achieve optimal adjustments of DGO-L parameters and to evaluate progress during RAGT, it is important to know the Gait & Posture 35 (2012) 595–600 A R T I C L E I N F O Article history: Received 30 January 2011 Received in revised form 28 November 2011 Accepted 29 November 2011 Keywords: Robotics Rehabilitation Gait Biofeedback Lokomat Neurological disease A B S T R A C T Background: Neurological disorders lead to walking disabilities, which are often treated using robot- assisted gait training (RAGT) devices such as the driven gait-orthosis Lokomat. A novel integrated biofeedback system was developed to facilitate therapeutically desirable activities during walking. The aim of this study was to evaluate the feasibility to detect changes during RAGT by using this novel biofeedback approach in a clinical setting for patients with central neurological disorders. Methods: 84 subjects (50 men and 34 women, mean age of 58  13 years) were followed over 8 RAGT sessions. Outcome measures were biofeedback values as weighted averages of torques measured in the joint drives and independent parameters such as guidance force, walking speed, patient coefficient, session duration, time between sessions and total treatment time. Results: Joint segmented analysis showed significant trends for decreasing hip flexion activity (p  .003) and increasing knee extension activity (p  .001) during RAGT sessions with an intercorrelation of r = .43 (p  .001). Further associations among independent variables were not statistically significant. Conclusion: This is the first study that evaluates the Lokomat integrated biofeedback system in different neurological disorders in a clinical setting. Results suggest that this novel biofeedback approach used in this study is not able to detect progress during RAGT. These findings should be taken into account when refining existing or developing new biofeedback strategies in RAGT relating to appropriate systems to evaluate progress and support therapist feedback in clinical settings. ß 2011 Elsevier B.V. All rights reserved. * Corresponding author at: Bern University of Applied Sciences, Institute for Rehabilitation and Performance Technology, Pestalozzistrasse 20, 3400 Burgdorf, Switzerland. Tel.: +41 34 426 41 95. E-mail addresses: stolleroliver@gmail.com, oliver.stoller@bfh.ch (O. Stoller). Contents lists available at SciVerse ScienceDirect Gait & Posture jo u rn al h om ep age: ww w.els evier.c o m/lo c ate/g aitp os t 0966-6362/$ – see front matter ß 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.gaitpost.2011.11.031