By Rocco Salvatore Calabrò, MD, PhD; Alfredo Manuli, MSc; Antonino Leo, MSc; Margherita Russo, MD, PhD; Antonino Naro, MD, PhD; and Placido Bramanti, MD
IRCCS Centro Neurolesi “Bonino-Pulej0,” Messina, Italy
Funding/financial disclosures. The author has no conflicts of interest relevant to the content of this letter. No funding was received for the preparation of this letter.
Innov Clin Neurosci. 2018;15(9–10):11–13
Gait abnormalities due to neurological disorders are often disabling and can negatively affect a patient’s quality of life.1 Regaining the ability to walk is considered one of the primary objectives of neurorehabilitation. An effective rehabilitation program is commonly focused on task-specific training, in which the patient practices all walking movements, repetitively and in a physically correct manner, to induce improvements of motor cortex representations, recover and strengthen the capabilities of the muscle groups, and improve coordination.2
Conventional rehabilitation techniques have limits that can undermine usability and efficacy. In fact, maintaining the spatial and temporal symmetry between the steps when utilizing conventional rehabilitation techniques becomes increasingly difficult for the patient, and ensuring the repeatability of the exercises in the same initial conditions is almost impossible due to the risk of overstraining the physiotherapist. To overcome these problems, robotic devices have been developed to reduce the required effort and time needed for proper rehabilitation, improve the reproducibility of the kinematics during the gait cycle, and increase the volume of the motor exercises.3
A robot is defined as a reprogrammable, multi-functional manipulator designed to move material, parts, or specialized devices through variable programmed motions to accomplish a task. However, to be considered a robot, an electromedical device should follow the three law for neurorobotics: 1) it cannot injure a patient or allow a patient to come to harm; 2) it must obey the orders given to it by the therapist, except when such orders conflict with the first law; and 3) it must adapt its behavior to a patient’s abilities in a transparent manner, as long as this does not conflict with the first or second law.4
Rehabilitative robotic devices can be classified into stationary and overground systems.3 The former are implemented using a fixed structure combined with a moving ground platform and have been developed with the aim to automate the traditional rehabilitation therapies that typically focus on treadmill training to improve motor skills. According to the types of adopted mobile platforms, two groups of stationary walking systems can be distinguished: treadmill gait trainers (e.g., Lokomat®, Hocoma AG, Switzerland) and programmable foot end-effector trainers (e.g. G-EO-System™, Reha Technology AG, Switzerland). The overground devices (e.g., Ekso-GT®, Ekso Bionics, Richmond, California) consist of a moving base robot that helps patients practice gait, posture, and balance exercises in a safe manner that the patients control themselves rather than following predetermined movement patterns (Figure 1).
There is growing evidence supporting the effectiveness of robotics in improving gait parameters and related functional outcomes in patients following severe acquired brain injury, spinal cord injury, Parkinson’s disease, multiple sclerosis, and other neurological diseases.3 However, most of the current literature is focused on patients affected by stroke.5 A recent systematic review provides evidence that, in combination with physiotherapy, the use of electromechanical-assisted gait training devices increases the chance of regaining independent walking ability after stroke in the subacute phase.6 Nonetheless, there is evidence that robotic-assisted gait rehabilitation is effective in improving not only motor function (including gait, balance and muscle force, walking ability, and velocity) but also mood, cognition, and coping strategies in patients affected by chronic stroke.7
Correct use of gait rehabilitation techniques, both conventional and new, relies on proper patient selection and consideration of the rehabilitation phase of each patient. For example, patients with severe motor leg impairments would benefit the most from robot-assisted therapy combined with conventional therapy, whereas those with greater voluntary motor function in the affected limb could likely achieve good functional recovery using conventional therapy alone.8
It is worth noting that the best functional recovery following a neurological injury has been observed using robotic rehabilitation combined with virtual reality (VR).9–10 VR represents a valid tool to augment the repetitive practice and the motivation to endure practice; to promote visual, auditory, and tactile input and motor learning; and to strengthen feedback about performance.9 Moreover, VR is thought to entrain the mirror neuron system by way of visuomotor stimulation as the user observes the human avatar walking on the screen. Such information recalls stored motor plans and potentiates the motor performance.10 A significant contribution of VR to robotic rehabilitation is the improvement in attention and motivation (as the integrated biofeedback system monitors the patient’s gait and provides real-time visual performance feedback to stimulate the patient’s active participation) and mood (as it is well known that depression might worsen cognitive impairment).9–10
In conclusion, robot-assisted gait rehabilitation can increase the length, intensity, and the number of physiotherapy sessions, thereby improving patient outcomes, which in turn can reduce therapist burden and healthcare costs. Clinicians should consider implementing this technology as a means to potentiate the functional recovery of patients affected by neurological disorders.
- Nonnekes J, Goselink RJM, R?ži?ka E, et al. Neurological disorders of gait, balance and posture: a sign-based approach. Nat Rev Neurol. 2018;14:183–189.
- Van Peppen RP, Kwakkel G, Wood-Dauphinee S, et al. The impact of physical therapy on functional outcomes after stroke: what’s the evidence? Clin Rehabil. 2004;18:833–862
- Calabrò RS, Cacciola A, Bertè F, et al. Robotic gait rehabilitation and substitution devices in neurological disorders: where are we now? Neurol Sci. 2016; 37:503–514.
- Iosa M, Morone G, Cherubini A, Paolucci S. The three laws of neurorobotics: a review on what neurorehabilitation robots should do for patients and clinicians. J Med Biol Eng. 2016;36:1–11.
- Bruni MF, Melegari C, De Cola MC, et al. What does best evidence tell us about robotic gait rehabilitation in stroke patients: A systematic review and meta-analysis. J Clin Neurosci. 2018;48:11–17.
- Mehrholz J, Elsner B, Werner C, et al. Electromechanical-assisted training for walking after stroke: updated evidence. Stroke. 2013;44:e127–128.
- Calabrò RS, De Cola MC, Leo A, et al. Robotic neurorehabilitation in patients with chronic stroke: psychological well-being beyond motor improvement. Int J Rehabil Res. 2015;38:
- Morone G, Paolucci S, Cherubini A, et al. Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics. Neuropsychiatr Dis Treat. 2017;13:1303–1311
- Calabrò RS, Naro A, Russo M, et al. The role of virtual reality in improving motor performance as revealed by EEG: a randomized clinical trial. J Neuroeng Rehabil. 2017;14:53.
- Calabrò RS, Russo M, Naro A, et al. Robotic gait training in multiple sclerosis rehabilitation: Can virtual reality make the difference? Findings from a randomized controlled trial. J Neurol Sci. 2017;377:25–30.
Rocco Salvatore Calabrò, MD, PhD; Alfredo Manuli, MSc; Antonino Leo, MSc; Margherita Russo, MD, PhD; Antonino Naro, MD, PhD; and Placido Bramanti, MD