The efficacy of the exoskeleton ExoAtlet to restore walking in patients with multiple sclerosis

Котов, С. В.; Lijdvoy, V. Yu.; Sekirin, A.; Petrushanskaya, K. A.; Pismennaya, E V

doi:10.17116/jnevro201711710241-47

Cited by 20 publications

(5 citation statements)

References 15 publications

(13 reference statements)

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“…On the other hand, exoskeletons (see Table 4) are classified as "treadmill-based", like Lokomat [99] (Hocoma, Volketswil, Switzerland), LokoHelp [100] (Woodway, Waukesha, WI, USA), LOPES [101], and ALEX [102], and as devices that directly allow for "overground walking", such as Ekso [103] (Ekso Bionics, Richmond, VA, USA), HAL (Cyberdyne, Tsukuba, Japan), Rewalk [104] (Rewalk Robotics, Marlborough, MA, USA), and Indego Therapy [105] (Parker Hannifin, Macedonia, OH, USA). In this category, new emerging devices include EXOATLET [106] (Exoatlet, Moscow, Russia), PhoeniX (SuitX, Berkeley, CA, USA), for which limited information is currently available, and REX (REX Bionics, London, UK). In addition, some of these exoskeletons, such as Lokomat and REX, have been employed to develop brain machine interfaces (BMIs) (for a review, see [107]).…”

Section: Robotic Neurorehabilitation For the Lower Limbmentioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots' capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients' recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.In the last decades, innovative robotic technologies have been developed in order to effectively help clinicians during the neurorehabilitation process. The term "robotic technology" in this application domain refers to any mechatronic device with a certain degree of intelligence that can physically intervene on the behavior of the patient, optimizing and speeding up his/her sensorimotor recovery. The two key capabilities of these robots are: (1) Assessing the human sensorimotor function; and (2) re-training the human brain in order to improve the patient's quality of life. However, most of the studies in this field have been focused more on the development of the devices, whereas less effort was made on maximizing their efficacy for promoting recovery. The main challenge consists of designing effective training modalities, supported by appropriate control strategies. Thus, each robotic device supports a pre-defined training modality depending on the low-level control strategy implemented and also on the residual abilities of each patient. Usually, most of the rehabilitation devices implement a passive training modality (robot-driven, position control strategy), where the robot imposes the trajectories, and an active training modality (patient-driven), where the robot modulates its trajectory in response to the subject's intention to move [7,8]. However, among all the different training modalities, the most relevant is the assistive one. Assistive controllers help participants to move their impaired limbs according to the desired postures during grasping, reaching, or walki...

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Section: Robotic Neurorehabilitation For the Lower Limbmentioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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“…The ExoAtlet wearable exoskeletal robot (ExoAtelt-I, ExoAtlet ASIA, Republic of Korea) was approved for use at home under supervision [26]. The robot consists of a metal brace that partially supports the legs and upper body, a motor that provides coordinated movement of the hips, knees and ankles, and a backpack containing a tilt sensor, a computer and an electric power supply.…”

Section: Exoatlet Exoskeletalwearable Robot Gait Trainingmentioning

confidence: 99%

Analysis of the Gait Characteristics and Usability after Wearable Exoskeleton Robot Gait Training in Incomplete Spinal Cord Injury Patients with Industrial Accidents: A Preliminary Study

Bae

Kim

Lee

et al. 2022

Phys Ther Rehabil Sci

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The aim of this study was to investigate of the foot plantar pressure and usability after gait training using the ExoAtlet wearable exoskeleton robot in an incomplete spinal cord injury (SCI) patient. Design: A case study Methods: Six participants with an asymmetry in motor and sensory function completed the gait training using ExoAtlet wearable exoskeleton robot for 15 sessions, five per weeks, 3weeks. They were divided into two groups (low and high strength group) and group differences were evaluated about session at stating of gait, gait distance at final session and foot plantar pressures and useability after training. Results: Low strength group was faster than high strength group on adaptation of robot gait. And high strength group increased faster than low strength group on the gait distance during training. In standing and gait, weaker leg was higher than stronger leg on mean foot plantar pressure in low strength group. And stronger leg was higher than weaker leg on foot plantar pressure in high strength group. The length of the anterior-posterior trajectory of the center of pressure during gait was similar in low strength group, but different in high strength group. useability was positive about ExoAtlet wearable exoskeleton gait after training. Conclusions: ExoAtlet wearable exoskeleton robot gait training was positive about improving gait in all participants regardless of differences in severity of symptoms and gait abnormalities.

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“…CCA finds the weight vectors, WI and WƟ, which maximize the correlation between I and Ɵ, by solving the following optimization problem. (8) where ρn are the CCA coefficients obtained with the angle of reference signals being Ɵ1, Ɵ2, ..., Ɵn. The maximum of ρ with respect to WI and WƟ is the maximum canonical correlation.…”

Section: ) First Study: Offline Assessment Of Reliabilitymentioning

confidence: 99%

“…One of the most difficult cases addressed by researchers in rehabilitation robotics is the training of ground running with exoskeleton, such as HAL [3] of the University of Tsukuba, EXPOS of Sogang University [4]. ReWalk [5], Rex Bionics [6], Indigo [7], Exoatlet [8], Wandercraft [9], among others. There are two known control strategies for these devices (i) impedance or admittance control, which are generally predetermined and do not consider the user's physical condition [10], [11] (ii) electromyogram (EMG)-based control which rely on the detection of muscle activation.…”

mentioning

confidence: 99%

Biokinematic Control Strategy for Rehabilitation Exoskeleton Based on User Intention

Charafeddine¹,

Chevallier²,

Alfayad³

et al. 2019

IJMO

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Rehabilitation exoskeletons require a control interface for the direct transfer of mechanical power and exchange of information in order to assist the patient in his/her movements. By using co-contraction indexes (CCI), it is possible to accurately characterize human movement and joint stability. But when dealing with human movement disorders, no existing index allows to achieve neuro-motor control with bio-kinematic sensors. Thus, we propose a neuro-motor interactive method for lower-body exoskeleton control. A novel dynamic index called neuro-motor index (NMI) is introduced to estimate the relation between muscular co-contraction derived from electromyography signals (EMG) and joint angles. To estimate the correlation in the state space and enhance the precision of the NMI, we describe an estimation method relying on a two-way analysis of canonical correlation (CCA). A thorough assessment is presented, by conducting two studies on control subjects and on patients with abnormal gait in a medical environment. i) An offline study on control patients showed that NMI captures the complex variation induced by changing walking speed more accurately than CCI, ii) an online study, applied on successive gait cycles of patients with abnormal walk indicates that the existing CCI have a low accuracy related with joint angles while it is significantly higher with NMI. Index Terms-Exoskeleton for rehabilitation, co-contraction index, neuro-motor control, bio kinematic. I. INTRODUCTION The rehabilitation exoskeleton is a mechatronic device, worn by the neurological patient, designed to increase physical performance and fitted to the shape and function of the human body. This prosthesis is used to provide a high-intensity training for human limbs on user-specific basis, to help the recovery of a neurological disease or disorder [1], [2]. The exoskeleton works mechanically in parallel with the human body [1] and can be controlled in a passive or active way. Typically, a complete exoskeleton, consists of a sensory system that acquires the physiological signals of interest, a processing stage to extract the relevant parameters that can be used to control the exoskeleton accordingly. The main Manuscript

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The efficacy of the exoskeleton ExoAtlet to restore walking in patients with multiple sclerosis

Cited by 20 publications

References 15 publications

Perspectives and Challenges in Robotic Neurorehabilitation

Perspectives and Challenges in Robotic Neurorehabilitation

Analysis of the Gait Characteristics and Usability after Wearable Exoskeleton Robot Gait Training in Incomplete Spinal Cord Injury Patients with Industrial Accidents: A Preliminary Study

Biokinematic Control Strategy for Rehabilitation Exoskeleton Based on User Intention

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