Prospective, Blinded, Randomized Crossover Study of Gait Rehabilitation in Stroke Patients Using the Lokomat Gait Orthosis

Department of Neurology, Hospital Hochzirl, Zirl, Austria.
Neurorehabilitation and neural repair (Impact Factor: 3.98). 05/2007; 21(4):307-14. DOI: 10.1177/1545968307300697
Source: PubMed


Treadmill training with partial body weight support has been suggested as a useful strategy for gait rehabilitation after stroke. This prospective, blinded, randomized controlled study of gait retraining tested the feasibility and potential efficacy of using an electromechanical-driven gait orthosis (Lokomat) for treadmill training.
Sixteen stroke patients, mostly within 3 months after onset, were randomized into 2 treatment groups, ABA or BAB (A = 3 weeks of Lokomat training, B = 3 weeks of conventional physical therapy) for 9 weeks of treatment. The outcome measures were the EU-Walking Scale, Rivermead Motor Assessment Scale, 10-m timed walking speed, 6-minute timed walking distance, Motricity Index, Medical Research Council Scale of strength, and Ashworth Scale of tone.
The EU-Walking Scale, Rivermead Motor Assessment Scale, 6-minute timed walking distance, Medical Research Council Scale, and Ashworth Scale demonstrated significantly more improvement during the Lokomat training phase than during the conventional physical therapy phase within each 3-week interval.
Despite the small number of patients, the present data suggest that the Lokomat robotic assistive device provides innovative possibilities for gait training in stroke rehabilitation while eliminating prolonged repetitive movements in a nonergonomic position on the part of the physical therapist.

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    • "A variety of robotic rehabilitation devices have been proposed for gait rehabilitation after stroke (eg. [5]–[8]), but have only produced moderate results compared to conventional physiotherapy [9]–[12]. Currently, there is no evidence that robotic gait training is superior to conventional physiotherapy for either chronic or subacute stroke patients [13]. "
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    ABSTRACT: Robotic devices have been used in a variety of rehabilitation protocols, including gait rehabilitation after stroke. However, robotic intervention in gait therapy has only produced moderate results compared to conventional physiotherapy. We suggest a novel approach to robotic interventions which takes advantage of inter-limb coordination mechanisms. We hypothesize the existence of a mechanism of inter-leg coordination that may remain intact after a hemiplegic stroke that may be utilized to obtain functional improvement of the impaired leg. One of the most significant advantages of this approach is the safety of the patient, since this does not require any direct manipulation of the impaired leg. In this paper, we focus on designing and applying unilateral perturbations that evoke contralateral leg motions through mechanisms of inter-leg coordination. Real-time control of floor stiffness is utilized to uniquely differentiate force and kinematic feedback, creating novel perturbations. We present results of repeatable and scalable evoked muscle activity of the contralateral tibialis anterior muscle through unilateral stiffness perturbations. We also present a mathematical model that accurately describes the relationship between the magnitude of the stiffness perturbation and the evoked muscle activity, that could result in model-based rehabilitation strategies for impaired walkers. The novel methods and results presented in this paper set the foundation for a paradigm shift of robotic interventions for gait rehabilitation.
    Proceedings - IEEE International Conference on Robotics and Automation 06/2015; 2015:6463-6468. DOI:10.1109/ICRA.2015.7140107
    • "Improvements in gait and balance have been reported in the literature for patients with spinal cord injury (Hornby et al., 2005b; Wirz et al., 2005), Parkinson's disease (Lo et al., 2010), multiple sclerosis (Beer et al., 2008; Lo and Triche, 2008) and stroke (Mayr et al., 2007). Although a recent update of a Cochrane review suggests a better outcome (independent walking) of subacute stroke patients after robotassisted gait training (RAGT) in combination with physiotherapy compared to physiotherapy alone (Mehrholz et al., 2013), it is not proven at the moment that RAGT is more effective compared to other gait rehabilitation methods (Mehrholz and Pohl, 2012; Mehrholz et al., 2008, Clinical Biomechanics xxx (2015) xxx–xxx ☆ Test location and ethical commission: All measurements were performed at the Rehabilitation Centre St-Ursula, Jessa Hospital, Herk-de-Stad, Belgium. "

    Physiotherapy 05/2015; 101:e1461. DOI:10.1016/ · 1.91 Impact Factor
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    • "Locomotion training robots (LTRs) possess structures with smaller mass and liberate physicians' workforce, as the actuator can be applied for motion transmission. There are two kinds of LTRs, namely, traction type and exoskeleton type LTRs [3]. The traction type LTRs are commonly used with fixed platform to which the users cannot move relatively. "
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    ABSTRACT: This paper introduces a multiposture locomotor training device (MPLTD) with a closed-loop control scheme based on joint angle feedback, which is able to overcome various difficulties resulting from mechanical vibration and the weight of trainer to achieve higher accuracy trajectory. By introducing the force-field control scheme used in the closed-loop control, the device can obtain the active-constrained mode including the passive one. The MPLTD is mainly composed of three systems: posture adjusting and weight support system, lower limb exoskeleton system, and control system, of which the lower limb exoskeleton system mainly includes the indifferent equilibrium mechanism with two degrees of freedom (DOF) and the driving torque is calculated by the Lagrangian function. In addition, a series of experiments, the weight support and the trajectory accuracy experiment, demonstrate a good performance of mechanical structure and the closed-loop control.
    Advances in Mechanical Engineering 11/2014; 2014:1-10. DOI:10.1155/2014/173518 · 0.58 Impact Factor
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