- •Recovery of walking function is one of the main goals of patients after stroke.
- •RAGT may be considered a valuable tool in improving gait abnormalities.
- •The earlier the gait training starts, the better the motor recovery.
Posts Tagged Lokomat
[ARTICLE] Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies – Full Text
The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies.
ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected.
At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals.
The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®.
The number of technological devices that therapists can utilize to treat people with neurological impairments has grown substantially during the last decade. Alongside this growth in clinical use, research involving robotic therapy has grown rapidly. A search in Pubmed with the terms “robot” OR “robotic*” AND “rehabilitation” revealed 2225 hits (March 2017) with research markedly increasing after 2010. Despite this increase in research activity and clinical use, the effectiveness of robot-assisted interventions in neurorehabilitation is still in debate. While in some patient populations, for example adults with stroke, meta-analyses have shown that robotic interventions for the lower and upper extremity can be beneficial [1, 2], current evidence is much less convincing in other patient groups, such as spinal cord injury (SCI), traumatic brain injury (TBI), multiple sclerosis (MS) and cerebral palsy (CP).
When comparing the effectiveness of robot-assisted gait training (RAGT) to conventional interventions of similar dosage in adult patients after SCI, it appears that neither intervention is superior [3, 4]. In other populations, such as MS, a small number of pilot studies have been conducted, and a review  concluded that the evidence for the effectiveness remained inconclusive. In adult patients with TBI, to our knowledge, there is only one randomized controlled trial that investigated the effectiveness of RAGT . While RAGT improved gait symmetry compared to manually assisted body-weight supported treadmill training, improvements in other gait parameters were not different between the interventions. In children with CP, the body of evidence is similarly small, as only two randomized trials were found [7, 8]. To the authors’ knowledge, there are no randomized controlled trials in children with other diagnoses. Studies comparing effectiveness between different patient groups are lacking.
One important factor leading to the lack of conclusive research is the relatively small number of available centers and participating patients and consequently the small statistical power of attempted studies. Multicenter collaborations are needed to achieve adequate number of participants. Several of the limitations in the evidence of the application of RAGT arise from patient selection criteria and use of different, poorly described and/or low-dosed training protocols. For example, when systematically reviewing the literature in children, we found no paper describing a training protocol on how to apply a robot for rehabilitation of gait . Most of the systematic reviews mentioned that it is extremely difficult to pool results from studies due to the large variability in treatment duration and frequency, contents of the training and inclusion criteria of the patients. For children with CP, an expert team was created to formulate goals, inclusion criteria, training parameters and recommendations on including RAGT in the clinical setting, to assist therapists who train children with CP with the Lokomat® (Hocoma AG, Volketswil, Switzerland) . Such information could be used as a first step in defining training protocols, but this information is missing for most other patient groups.
While randomized controlled trials are usually considered the “gold standard” in building solid evidence in the field of medicine, it is often difficult for rehabilitation specialists working in the clinical environment to interpret the findings with respect to the population of patients they treat on a daily basis. Randomized controlled trials require a specialized team, a controlled setting and a strict selection of patients according to well defined inclusion and exclusion criteria. These criteria often select individuals most likely to benefit based on specific parameters and lack of co-morbidities. These narrow criteria may impact the ecological validity, as results only apply to a minority of patients. This was recently investigated by Dörenkamp et al.  who reported that the majority of patients in primary care (40% at the age of 50 years and at least two-thirds of the octogenarian population ) simultaneously suffered from multiple medical problems. Further, improvements in function might be less comparable to results described in randomized controlled trials and the treatment regimens used may not be applicable to patients with multiple comorbidities.
To overcome these issues, we established the Advanced Robotic Therapy Integrated Centers (ARTIC) network to collect data from patients using RAGT in a wide variety of clinical settings. ARTIC hopes to develop guidelines for usage as well as to answer scientific questions concerning the use of RAGT. While the ARTIC network includes a general patient population, other research networks focus on a specific disorder or diagnostic group (see, for example [12, 13]). ARTIC focuses on a common technological intervention – currently the driven gait orthosis Lokomat® – and aims to gather evidence for the efficient and effective use of robotic therapy. Variation in practice among ARTIC members together with collection of common data and outcome measurements will enable the group to draw strong, generalizable conclusions. Further goals include establishing standardized treatment protocols and increasing medical and governmental acceptance of robotic therapy. The aims of this paper are to introduce the ARTIC network to the clinical and research community, present initial data on the characteristics of included patients and compare these to those known from existing epidemiological data and interventional studies.[…]
Continue —> Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies | Journal of NeuroEngineering and Rehabilitation | Full Text
[Abstract] What does best evidence tell us about robotic gait rehabilitation in stroke patients: A systematic review and meta-analysis
Studies about electromechanical-assisted devices proved the validity and effectiveness of these tools in gait rehabilitation, especially if used in association with conventional physiotherapy in stroke patients.
The aim of this study was to compare the effects of different robotic devices in improving post-stroke gait abnormalities.
A computerized literature research of articles was conducted in the databases MEDLINE, PEDro, COCHRANE, besides a search for the same items in the Library System of the University of Parma (Italy). We selected 13 randomized controlled trials, and the results were divided into sub-acute stroke patients and chronic stroke patients. We selected studies including at least one of the following test: 10-Meter Walking Test, 6-Minute Walk Test, Timed-Up-and-Go, 5-Meter Walk Test, and Functional Ambulation Categories.
Stroke patients who received physiotherapy treatment in combination with robotic devices, such as Lokomat or Gait Trainer, were more likely to reach better results, compared to patients who receive conventional gait training alone. Moreover, electromechanical-assisted gait training in association with Functional Electrical Stimulations produced more benefits than the only robotic treatment (−0.80 [−1.14; −0.46], p > .05).
The evaluation of the results confirm that the use of robotics can positively affect the outcome of a gait rehabilitation in patients with stroke. The effects of different devices seems to be similar on the most commonly outcome evaluated by this review.
[ARTICLE] The role of virtual reality in improving motor performance as revealed by EEG: a randomized clinical trial – Full Text
Many studies have demonstrated the usefulness of repetitive task practice by using robotic-assisted gait training (RAGT) devices, including Lokomat, for the treatment of lower limb paresis. Virtual reality (VR) has proved to be a valuable tool to improve neurorehabilitation training. The aim of our pilot randomized clinical trial was to understand the neurophysiological basis of motor function recovery induced by the association between RAGT (by using Lokomat device) and VR (an animated avatar in a 2D VR) by studying electroencephalographic (EEG) oscillations.
Twenty-four patients suffering from a first unilateral ischemic stroke in the chronic phase were randomized into two groups. One group performed 40 sessions of Lokomat with VR (RAGT + VR), whereas the other group underwent Lokomat without VR (RAGT-VR). The outcomes (clinical, kinematic, and EEG) were measured before and after the robotic intervention.
As compared to the RAGT-VR group, all the patients of the RAGT + VR group improved in the Rivermead Mobility Index and Tinetti Performance Oriented Mobility Assessment. Moreover, they showed stronger event-related spectral perturbations in the high-γ and β bands and larger fronto-central cortical activations in the affected hemisphere.
The robotic-based rehabilitation combined with VR in patients with chronic hemiparesis induced an improvement in gait and balance. EEG data suggest that the use of VR may entrain several brain areas (probably encompassing the mirror neuron system) involved in motor planning and learning, thus leading to an enhanced motor performance.
Virtual reality (VR) is the simulation of a real environment generated by a computer software and experienced by the user through a human–machine interface . This interface enables the patient to perceive the environment as real and 3D (i.e., the sense of presence), thus increasing patient’s engagement (i.e., embodiment) . Hence, VR can be used to provide the patient with repetitive, task-specific training (as opposed to simply using a limb by chance) that are effective for motor learning functions [3, 4, 5, 6]. In fact, VR provides the patient with multisensory feedbacks that can potentiate the use-dependent plasticity processes within the sensory-motor cortex, thus promoting/enhancing functional motor recovery [7, 8, 9, 10, 11, 12, 13, 14]. Furthermore, VR can increase patients’ motivation during rehabilitation by decreasing the perception of exertion , thus allowing patients to exercise more effortlessly and regularly .
It is possible to magnify the sense of presence by manipulating the characteristics of the VR, including screen size, duration of exposure, the realism of the presentation, and the use of animated avatar, i.e., a third-person view of the user that appears as a player in the VR . About that, the use of an avatar may strengthen the use-dependent plastic changes within higher sensory-motor areas belonging to the mirror neuron system (MNS) [16, 17, 18]. In fact, the observation of an action, even simulated (on a screen, as in the case of VR), allows the recruitment of stored motor programs that would promote, in turn, movement execution recovery [19, 20]. These processes are expressed by wide changes in α and β oscillation magnitude at the electroencephalography (EEG) (including an α activity decrease and a β activity increase) across the brain areas putatively belonging to the MNS (including the inferior frontal gyrus, the lower part of the precentral gyrus, the rostral part of the inferior parietal lobule, and the temporal, occipital and parietal visual areas) [8, 9, 21, 22].
In the last years, motor function recovery has benefited from the use of robotic devices. In particular, robot-assisted gait training (RAGT) provides the patient with highly repeated movement execution, whose feedback, in turn, permits to boost the abovementioned use-dependent plasticity processes . RAGT has been combined with VR to further improve gait in patients suffering from different neurologic diseases . Nonetheless, the knowledge of the neurophysiologic substrate underpinning neurorobotic and VR interaction is still poor [25, 26]. Indeed, a better understanding of this interaction would allow physician to design more personalized rehabilitative approaches concerning the individual brain plasticity potential to be harnessed to gain functional recovery .
The relative suppression of the μ rhythm is considered as the main index of MNS activity . Nonetheless, conjugating VR and neurorobotic could make brain dynamics more complex, because of many factors related to motor control and psychological aspects come into play, including intrinsic motivation, selective attention, goal setting, working memory, decision making, positive self-concept, and self-control. Altogether, these aspects may modify and extend the range of brain rhythms deriving from different cortical areas related to MNS activation by locomotion, including theta and gamma oscillations [29, 30, 31]. Specifically, theta activity has been related to the retrieval of stored motor memory traces, whereas the gamma may be linked to the conscious access to visual target representations [30, 31]. Such broadband involvement may be due to the recruitment of multiple brain pathways expressing both bottom-up (automatic recruitment of movement simulation) and top-down (task-driven) neural processes within the MNS implicated in locomotion recognition . A recent work has shown that observed, executed, and imagined action representations are decoded from putative mirror neuron areas, including Broca’s area and ventral premotor cortex, which have a complex interplay with the traditional MNS areas generating the μ rhythm .
Therefore, we hypothesized that the combined use of VR and RAGT may induce a stronger and wider modification of the brain oscillations deriving from the putative MNS areas, thus augmenting locomotor function gain [34, 35]. The aim of our pilot randomized clinical trial was to understand the neurophysiological basis underpinning gait recovery induced by the observation of an animated avatar in a 2D VR while performing RAGT by studying the temporal patterns of broadband cortical activations.[…]
[ARTICLE] Hemorrhagic versus ischemic stroke: Who can best benefit from blended conventional physiotherapy with robotic-assisted gait therapy? – Full Text
Contrary to common belief of clinicians that hemorrhagic stroke survivors have better functional prognoses than ischemic, recent studies show that ischemic survivors could experience similar or even better functional improvements. However, the influence of stroke subtype on gait and posture outcomes following an intervention blending conventional physiotherapy with robotic-assisted gait therapy is missing.
This study compared gait and posture outcome measures between ambulatory hemorrhagic patients and ischemic patients, who received a similar 4 weeks’ intervention blending a conventional bottom-up physiotherapy approach and an exoskeleton top-down robotic-assisted gait training (RAGT) approach with Lokomat.
Forty adult hemiparetic stroke inpatient subjects were recruited: 20 hemorrhagic and 20 ischemic, matched by age, gender, side of hemisphere lesion, stroke severity, and locomotor impairments. Functional Ambulation Category, Postural Assessment Scale for Stroke, Tinetti Performance Oriented Mobility Assessment, 6 Minutes Walk Test, Timed Up and Go and 10-Meter Walk Test were performed before and after a 4-week long intervention. Functional gains were calculated for all tests.
Hemorrhagic and ischemic subjects showed significant improvements in Functional Ambulation Category (P<0.001 and P = 0.008, respectively), Postural Assessment Scale for Stroke (P<0.001 and P = 0.003), 6 Minutes Walk Test (P = 0.003 and P = 0.015) and 10-Meter Walk Test (P = 0.001 and P = 0.024). Ischemic patients also showed significant improvements in Timed Up and Go. Significantly greater mean Functional Ambulation Category and Tinetti Performance Oriented Mobility Assessment gains were observed for hemorrhagic compared to ischemic, with large (dz = 0.81) and medium (dz = 0.66) effect sizes, respectively.
Overall, both groups exhibited quasi similar functional improvements and benefits from the same type, length and frequency of blended conventional physiotherapy and RAGT protocol. The use of intensive treatment plans blending top-down physiotherapy and bottom-up robotic approaches is promising for post-stroke rehabilitation.
[ARTICLE] Immediate affective responses of gait training in neurological rehabilitation: A randomized crossover trial – Full Text HTML
Objective: To examine the immediate effects of physical therapy and robotic-assisted gait training on affective responses of gait training in neurological rehabilitation.
Design: Randomized crossover trial with blinded observers.
Patients: Sixteen patients with neurological disorders (stroke, traumatic brain injury, spinal cord injury, multiple sclerosis).
Methods: All patients underwent 2 single treatment sessions: physical therapy and robotic-assisted gait training. Both before and after the treatment sessions, the self-report Mood Survey Scale was used to assess the effects of the treatment on distinct affective states. The subscales of the Mood Survey Scale were tested for pre–post changes and differences in effects between treatments, using non-parametric tests.
Results: Fourteen participants completed the study. Patients showed a significant increase in activation (r = 0.55), elation (r = 0.79), and calmness (r = 0.72), and a significant decrease in anger (r = 0.64) after robotic-assisted gait training compared with physical therapy.
Conclusion: Affective responses might be positively influenced by robotic-assisted gait training, which may help to overcome motivational problems during the rehabilitation process in neurological patients.
Patients with neurological impairment are known to have reduced quality of life and increased risk for depressive symptoms, which may hinder their ability to perform daily rehabilitation programmes, such as physical therapy (PT) or robotic-assisted gait training (RAGT) (1). During the continuum of rehabilitation it is necessary to consider factors such as choice and enjoyment in order to determine specifically how an individual would participate in rehabilitation programmes. The inclusion of participation scales is recommended when assessing the outcome of rehabilitation programmes (2). According to Self-Determination Theory (3), positive affective responses (e.g. activation, elation, or calmness) are connected with high intrinsic motivation and are an important regulation process in human behaviour. Therefore affective responses to the treatment sessions, as defined by Ekkekakis & Petruzello (4), might be important predictors of motivation, adoption, and maintenance of treatment regimes in the rehabilitation process.
Fatigue is a common and distressing complaint among people with neurological impairment (5). Patients often are afraid that engagement in exercise may increase fatigue (6). In patients with traumatic brain injury, “lack of energy” was rated as 1 of the top 5 problems for participation (7). Therefore it is important to emphasize that it is more likely that a higher level of energy will be achieved after exercise (8, 9). Although not yet a widely recognized determinant of exercise behaviour, affective valence is viewed in psychology and behavioural economics as one of the major factors in human decision-making (10). Findings from exercise psychology have demonstrated that the affective components of pleasure and activation might be crucial for bridging the intention–behaviour gap at the beginning of engagement in exercise (10). Regular participation in physical activity, in the long-term, may be mediated by an individual’s belief in the exercise–psychological wellbeing association. It may also lead to anti-depressive effects (11). Both PT and RAGT can be considered as forms of physical activity; therefore one might speculate that the effects mentioned above could be transferred to neurological patients. While increases in energy and mood in response to a single bout of moderate intensity exercise have been shown in healthy people and several risk-groups (6, 8, 9), no such study has been carried out involving neurological patients.
To our knowledge, only 2 studies concerning RAGT and psychological effects have been published. Koenig et al. (12) described a method to observe mental engagement during RAGT. Recently, Calabro et al. (13) reported positive long-term effects of RAGT on mood and coping strategies in a case study. To our knowledge, apart from these studies, affective responses have not been researched in PT or RAGT.
Thus, the aim of this study was to determine, for patients with neurological impairment: (i) whether a single session of PT and RAGT has immediate effects on affective responses (e.g. activation, elation, or calmness) and; (ii) whether possible affective responses differ between PT and RAGT.
[ARTICLE] Feasibility of using Lokomat combined with functional electrical stimulation for the rehabilitation of foot drop. – Full Text PDF
This study investigated the clinical feasibility of combining the electromechanical gait trainer Lokomat with functional electrical therapy (LokoFET), stimulating the common peroneal nerve during the swing phase of the gait cycle to correct foot drop as an integrated part of gait therapy.
Five patients with different acquired brain injuries trained with LokoFET 2-3 times a week for 3-4 weeks. Pre- and post-intervention evaluations were performed to quantify neurophysiological changes related to the patients’ foot drop impairment during the swing phase of the gait cycle. A semi-structured interview was used to investigate the therapists’ acceptance of LokoFET in clinical practice. The patients showed a significant increase in the level of activation of the tibialis anterior muscle and the maximal dorsiflexion during the swing phase, when comparing the pre- and post-intervention evaluations.
This showed an improvement of function related to the foot drop impairment. The interview revealed that the therapists perceived the combined system as a useful tool in the rehabilitation of gait. However, lack of muscle selectivity relating to the FES element of LokoFET was assessed to be critical for acceptance in clinical practice.
[Abstract] Robotic gait rehabilitation and substitution devices in neurological disorders: where are we now? – Springer
Gait abnormalities following neurological disorders are often disabling, negatively affecting patients’ quality of life. Therefore, regaining of walking is considered one of the primary objectives of the rehabilitation process. To overcome problems related to conventional physical therapy, in the last years there has been an intense technological development of robotic devices, and robotic rehabilitation has proved to play a major role in improving one’s ability to walk.
The robotic rehabilitation systems can be classified into stationary and overground walking systems, and several studies have demonstrated their usefulness in patients after severe acquired brain injury, spinal cord injury and other neurological diseases, including Parkinson’s disease, multiple sclerosis and cerebral palsy.
In this review, we want to highlight which are the most widely used devices today for gait neurological rehabilitation, focusing on their functioning, effectiveness and challenges.
Novel and promising rehabilitation tools, including the use of virtual reality, are also discussed.
Robotic support has gained more and more interest in rehabilitation of human haptic behavior, e.g. after stroke. First types of rehabilitation robots were intended to replace repetitive movements performed by a physiotherapist by guiding the patient along a physiological reference trajectory. The robot has the advantage of an accurate and repetitive movement while being resistant to any type of fatigue.
New understanding of motor learning shows that active participation of the patient is an essential element of rehabilitation success. A rehabilitation robot should therefore be just as cooperative as the physiotherapist and enhance the patient’s activity. That means that they should only support the patient if needed. It has also been shown that perturbations such as increasing the error in the patient’s movement can progress the rehabilitation procedure more quickly than only “guiding” the patient to perform the correct movement. This form of therapy has some limitations however, if the patient is not able to apply the necessary forces for the movement. In this case the robot should give appropriate support, for example by providing partial weight support of the patient’s arm if the patient is not able to support their own weight. This simulated weightlessness is able to compensate for muscle disabilities and increase the range of motion during training sessions.
Furthermore, a rehabilitation robot can support the patient during specific tasks by recognizing movement deficiencies and disabilities. The robot supports as much as needed and as little as possible. Such a controller has been implemented in the armrobot ARMin (Figure 1, left). While the user is playing a ping-pong game, the robot is able to support the user as much as needed. In human gait rehabilitation, controller design is more restricted for the sake of security. In the Lokomat (Figure 1, right), path controlling is employed to ensure safe and still self-motivated walking. The path controlling method provides a tunnel for joint angles within which the patient can move. As soon as the patient exceeds the pre-set path trajectory limits, the robot pushes the patient back into the right direction. Figure 2 illustrates and explains the concept of path controlling. Another concept is employed in virtual model control (VMC) which aims at maximum patient activity and only supports selectively chosen characteristics such as length or height of the patient’s stride.
All of those control strategies require the robot to assist-as-needed. The assistance can be interpreted as a virtual helping hand. These virtually created worlds are able to display different forms, from free user-performed movements (no help) to resistance against “wrong” user movements (support), or even guiding the patient through their movement completely. In case of the patient being able to self-perform movements correctly, ideally, the robot should not be felt. This behavior is called transparency.
In addition to movement support, a rehabilitation robot is able to display a virtual world which the user can interact with. This is used for simulating activities if daily living (ADL) such as cooking. The representation of a virtual environment requires the possibility of displaying different virtual objects. Especially hard objects are important. Such requirements for the control of a hard environment differ a lot from those for the control of a free, transparent environment. Two different actuator and controller concepts are optimal to be employed to display a hard or soft environment respectively. The two strategies are called impedance and admittance control and will be the central part of this exercise.
Furthermore, we have to make sure that the human-robot-interaction is safe and secure, i.e. the robot should also be able to navigate a totally passive patient. Therefore, the actuators must fulfill some requirements on power and torque. This includes high transmission ratios, which additionally increase the reflected inertia of the drives. High robot inertia lowers the reachable transparency of the robot. Another important point is backdriveability, which makes the robot movable when the robot is not powered at all. This is an important fact e.g. for the case of an emergency stop.
To sum up, the design and choice of the hardware as well as the software implementation should balance each other. The robot has to bring enough forces and moments to support the patient. A strong (therefore heavy) robot arm is well able to display a hard virtual object such as a wall. On the other hand, the robot should be backdriveable and therefore be as lightweight as possible to easily display transparency. Inertia and mass of a strong (heavy) motor in the system make it difficult to display free environment such as air. Besides the choice of the hardware, the choice of the control strategy is an important fact, too. We will focus on two different strategies of how to display a virtual environment and discuss the concepts of impedance and admittance control.
[ARTICLE] Robot-assisted gait training improves motor performances and modifies Motor Unit firing in poststroke patients.
BACKGROUND: Robotics and related technologies are realizing their promise to improve the delivery of rehabilitation therapy but the mechanism by which they enhance recovery is still unknown. The electromechanical-driven gait orthosis Lokomat has demonstrated its utility for gait rehabilitation after stroke.
AIM: To test the efficacy of Lokomat in gait retraining and to investigate the neurophysiological mechanisms underlying the recovery process.
DESIGN: Case series study.
SETTING: Unit of Neurorehabilitation of a University Hospital.
POPULATION: Fifteen patients with post-stroke hemiparesis.
METHODS: Patients underwent a six weeks rehabilitative treatment provided by Lokomat.
The outcome measures were: Fugl-Meyer Motor Scale (FMMS), Berg Balance Scale (BBS), 10 metres Walking Test (10mWT), Timed Up and Go test (TUG), 6 Minute Walking Test (6MWT). Strength and Motor Unit firing rate of vastus medialis (VM) were analyzed during isometric knee extension through an isokinetic dynamometer and surface EMG recording.
RESULTS: An increase of duration and covered distance, a decrease of body weight support and guidance force on the paretic side along the sessions were observed. The FMMS, the BBS, the TUG and the 6MWT demonstrated a significant improvement after the training. No increase of force was observed whereas a significant increase of firing rate of VM was recorded.
CONCLUSION: The evidence that the improvement of walking ability observed in our study determines a significant increase of firing rate of VM not accompanied by an increase of force could suggest an effect of training on motorneuronal firing rate that thus contributes to improve motor control.
CLINICAL REHABILITATION IMPACT: Given the current wide use of robotics in gait retraining after stroke, our approach can contribute to clarify the mechanisms underlying its rehabilitative impact so as to incorporate the findings of evidence-based practice into appropriate treatment plans for persons poststroke.
[ARTICLE] Robotic neurorehabilitation in patients with chronic stroke: psychological well-being beyond motor improvement.
Although gait abnormality is one of the most disabling events following stroke, cognitive, and psychological impairments can be devastating. The Lokomat is a robotic that has been used widely for gait rehabilitation in several movement disorders, especially in the acute and subacute phases.
The aim of this study was to evaluate the effectiveness of gait robotic rehabilitation in patients affected by chronic stroke. Psychological impact was also taken into consideration.
Thirty patients (13 women and 17 men) affected by chronic stroke entered the study. All participants underwent neurological examination with respect to ambulation, Ashworth, Functional Independence Measure, and Tinetti scales to assess their physical status, and Hamilton Rating Scale for Depression, Psychological General Well-being Index, and Coping Orientation to Problem Experienced to evaluate the Lokomat-related psychological impact before and after either a conventional treatment or the robotic training.
During each rehabilitation period (separated by a no-treatment period), patients underwent a total of 40 1 h training sessions (i.e. five times a week for 8 weeks). After the conventional treatment, the patients did not achieve a significant improvement in the functional status, except balance (P<0.001) and walking ability (P<0.01), as per the Tinetti scale. Indeed, after the robotic rehabilitation, significant improvements were detected in almost all the motor and psychological scales that we investigated, particularly for Psychological General Well-being Index and Coping Orientation to Problem Experienced. Manual and robotic-assisted body weight-supported treadmill training optimizes the sensory inputs relevant to step training, repeated practice, as well as neuroplasticity.
Several controlled trials have shown a superior effect of Lokomat treatment in stroke patients’ walking ability and velocity in particular. Therefore, our preliminary results proved that active robotic training not only facilitates gait and physical function but also the psychological status, even in patients affected by chronic stroke.