Posts Tagged robotic rehabilitation

[ARTICLE] A comparison of the effects and usability of two exoskeletal robots with and without robotic actuation for upper extremity rehabilitation among patients with stroke: a single-blinded randomised controlled pilot study – Full Text

Abstract

Background

Robotic rehabilitation of stroke survivors with upper extremity dysfunction may yield different outcomes depending on the robot type. Considering that excessive dependence on assistive force by robotic actuators may interfere with the patient’s active learning and participation, we hypothesised that the use of an active-assistive robot with robotic actuators does not lead to a more meaningful difference with respect to upper extremity rehabilitation than the use of a passive robot without robotic actuators. Accordingly, we aimed to evaluate the differences in the clinical and kinematic outcomes between active-assistive and passive robotic rehabilitation among stroke survivors.

Methods

In this single-blinded randomised controlled pilot trial, we assigned 20 stroke survivors with upper extremity dysfunction (Medical Research Council scale score, 3 or 4) to the active-assistive robotic intervention (ACT) and passive robotic intervention (PSV) groups in a 1:1 ratio and administered 20 sessions of 30-min robotic intervention (5 days/week, 4 weeks). The primary (Wolf Motor Function Test [WMFT]-score and -time: measures activity), and secondary (Fugl-Meyer Assessment [FMA] and Stroke Impact Scale [SIS] scores: measure impairment and participation, respectively; kinematic outcomes) outcome measures were determined at baseline, after 2 and 4 weeks of the intervention, and 4 weeks after the end of the intervention. Furthermore, we evaluated the usability of the robots through interviews with patients, therapists, and physiatrists.

Results

In both the groups, the WMFT-score and -time improved over the course of the intervention. Time had a significant effect on the WMFT-score and -time, FMA-UE, FMA-prox, and SIS-strength; group × time interaction had a significant effect on SIS-function and SIS-social participation (all, p < 0.05). The PSV group showed better improvement in participation and smoothness than the ACT group. In contrast, the ACT group exhibited better improvement in mean speed.

Conclusions

There were no differences between the two groups regarding the impairment and activity domains. However, the PSV robots were more beneficial than ACT robots regarding participation and smoothness. Considering the high cost and complexity of ACT robots, PSV robots might be more suitable for rehabilitation in stroke survivors capable of voluntary movement.

Introduction

Approximately 30–66% of stroke survivors suffer from upper extremity dysfunction, which leads to impediment of activities of daily living (ADL) and social participation [1]. Various interventions have been applied for upper extremity rehabilitation, and robotic rehabilitation has been recently popularised [2,3,4].

Robotic rehabilitation has potential advantages regarding the high repetition of specific tasks and interactivity, leading to active participation with less burden on therapists [25]. Recent systematic reviews have suggested the beneficial effects of robotic rehabilitation on upper extremity dysfunction among patients with stroke [46]. Veerbeek et al. described that robotic rehabilitation is more beneficial for the improvement of the motor control and strength of a paretic arm, but not for that of ADL, than is conventional therapy [6]. Furthermore, Mehrholz et al. demonstrated that robotic rehabilitation has more beneficial effects on ADL as well as on arm function and muscle strength compared to conventional therapy [4]. However, these conclusions should be considered cautiously because the robots that were included in these reviews were heterogenous: 28 and 24 different rehabilitation robots were included in the systemic reviews by Veerbeek et al. and Mehrholz et al., respectively. We recently showed that the use of end-effector and exoskeleton rehabilitation robots led to significant functional outcome differences stemming from distinct characteristics of the robots; this indicates that the differential effects might result from the inherent characteristics of the rehabilitation robot that was used [7]. In addition to the structural difference, the type of robotic control architecture (e.g., position, force, and impedance control) or robotic actuation (e.g., hydraulic power, pneumatic, and electric motor actuation) could also affect the therapeutic outcome [89]. Nonetheless, there is a lack of studies that examined the differential effects according to the characteristics of robots. If the discrepant effects during upper extremity rehabilitation are understood according to the characteristics of robots, more suitable robotic rehabilitation may be applied and provided to each patient.

Accordingly, robotic devices can be classified as active-assistive and passive robotic devices according to the training modality. A passive robot does not provide assistive force, while an active-assistive robot provides assistive force with robotic actuators when the user is unable to make active movements [10,11,12]. Robotic active assistance is thought to be beneficial for users without voluntary movement because they can be trained with according to an ideal path or speed. Nonetheless, active assistance using manipulation for upper limb rehabilitation is too complex to be adopted with ease because the upper extremities are composed of several joints and different muscles, which allow movements with multiple degrees of freedom. Moreover, musculoskeletal problems associated with stroke such as spasticity, contractures, deformity, or hemiplegic shoulder pain make the application of robotic assistance more difficult. Additionally, excessive dependence on assistive force might interfere with active learning and participation for users who can perform voluntary movement. Therefore, we hypothesised that an active-assistive robot does not make a meaningful difference in terms of upper extremity rehabilitation relative to that made by a passive robot. Thus, we aimed to explore whether there is a difference in clinical and kinematic outcomes between active-assistive and passive robots during robot-assisted upper extremity rehabilitation of patients with stroke showing a Medical Research Council (MRC) scale score of 3 or 4 for the paretic proximal upper limb. In addition, we assessed the usability of robotic assistance. To our knowledge, this is the first clinical trial to directly compare rehabilitative effects between active-assistive and passive robots.[…]

Continue

, , , , , , , , , , , , , ,

Leave a comment

[Abstract] Efficient FEM Based Optimization of a Parallel Robotic System for Upper Limb Rehabilitation

Abstract

Cardiovascular stroke (CVS) is one of the leading causes of motor disabilities worldwide, and unfortunately the number of cases keeps increasing, and will continue to increase until personnel shortages will make the motor rehabilitation procedure to be more challenging. The main solution for this is the automation of the rehabilitation process through the use of robotic technologies capable of providing high dosage and intensity training with minimal interference from the kinesiotherapy specialist. In this paper, the authors present a parallel robotic solution for the rehabilitation of the wrist joint. FEM based simulations are carried out on the most stressed/strained components to identify the reaction forces acting on them during the execution of a rehabilitation exercises. Furthermore the mechanical structure of the targeted components is optimized and placed under FEM analysis again to demonstrate the improvements that have been brought, while tests in medical environment are presented to validate the rehabilitation robotic system.

Source

, , , , , ,

Leave a comment

[BLOG POST] Technology: Filling the gaps in occupational and physical therapy

It is unsurprising that, as the population increases and ages, more therapists will be needed for rehabilitation, but the therapist numbers are not growing to match the need for therapy. The resulting deficit leaves even top rehabilitation centers at a loss; fewer therapy experts means less rehab time for patients.

The human element of therapy is undeniable—people need people in order to heal. However, time and physical effort is required to manually facilitate high-repetition therapy exercises desperately needed by patients and this limits their execution, even in world-class facilities. Therapists are the limiting factor in patient care simply because there are not enough experts and their physical resources are limited, especially in case of severely affected patients who require high physical support. This problem is only expected to worsen.

Technology, in the form of robotic rehabilitation, solves this issue elegantly by relieving therapists of the burden of attending to every repetition, allowing them to serve more patients, more efficiently, and with better outcomes.

In stroke and neuro rehabilitation, intensity is key

Many studies have shown that in various types of injuries, rehabilitation that includes hundreds to thousands of repetitions produce best clinical outcomes for upper and lower extremity movements. Task specificity and muscle reconditioning, in addition to neuroplasticity, are important factors influenced by intense, targeted, repetitive motor training.

A shocking study conducted in 2017 on spinal cord rehab patients found that:

  • As much at 40% of therapy time was dedicated to non-therapeutic actions, such as sling transfers and activity set up
  • Patients spent only 12-15 minutes in group-based rehab activity
  • Up to 2/3 of patients did not participate in group activities at all
  • The highest-repetition groups did not exceed 100 repetitions for occupational and physiotherapy combined
  • The daily repetitions were significantly lower than those require for muscle and neural improvements.1

Furthermore, the following table from a study of outpatients suffering from partial paralysis post-stroke shows less than 100 repetitions per session with the exception of walking steps.2

 

 

In animal studies, however, it was found that at least 400-600 repetitions are necessary to lead to structural neural changes for upper limb.1 There is clearly a significant disparity between the therapy needed for full recovery and the therapy available to patients. In addition, researchers surveyed 7 stroke survivors, 6 caregivers, and 20 rehab staff and found that outside of rehab:

“subsequent time was described as ‘dead and wasted.’ Main careers perceived stroke survivors felt ‘out of control … at everyone’s mercy’ and lacked knowledge of ‘what to do and why’ outside of therapy. Clinical staff perceived the stroke survivor’s ability to drive their own recovery was limited by the lack of ‘another place to go’ and the ‘passive rehab culture and environment’.”3

This passive rehab culture is a significant factor for reduced therapy outcomes. When dependent on limited rehab time for recovery, stroke patients feel unproductive and hopeless. More active therapy time can not only improve a patient’s daily function and physical health, but their mental health as well.

How Technology Can Help

It is clear that patients are not currently getting the volume and intensity of therapy required for ideal outcomes, and this not only has a physical cost, but also takes a mental toll on patients. Robot-assisted rehab can improve current therapy practices in the following ways:

  • Assisted gait training – research shows that robots can assist with “highly repetitive training of complex gait cycles, something a single therapist cannot easily do alone”4
  • Precision feedback – virtual reality and sophisticated measurements can provide precise movement feedback to leverage the recovering brain’s neuroplasticity and enhance proprioception5
  • High repetition and intensity – without constant physical assistance from a therapist, which then frees the therapist to perform other types of supporting tasks

While therapists cannot be replaced by robot-assisted rehabilitation technology, these tools can augment their practice to both reduce strain and fatigue in therapists and improve patient outcomes, sometimes making possible what was impossible previously, such as walking. Robot-assisted rehabilitation can reduce physical strain in therapists while providing for patients the high volume of repetitions needed to achieve best outcomes in therapy.

References:

1 Zbogar D, Eng JJ, Miller WC, Krassioukov AV, Verrier MC. Movement repetitions in physical and occupational therapy during spinal cord injury rehabilitation. Spinal Cord. 2017;55(2):172–179. doi:10.1038/sc.2016.129
2 Lang CE, MacDonald JR, Gnip C. Counting repetitions: an observational study of outpatient therapy for people with hemiparesis post-stroke. J Neurol Phys Ther. 2007 Mar;31(1):3-10. PubMed PMID: 17419883.
3 Eng XW, Brauer SG, Kuys SS, Lord M, Hayward KS. Factors Affecting the Ability of the Stroke Survivor to Drive Their Own Recovery outside of Therapy during Inpatient Stroke Rehabilitation. Stroke Res Treat. 2014;2014:626538. doi:10.1155/2014/626538
4 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. Published 2017 May 15. doi:10.2147/NDT.S114102
5 Turner DL, Ramos-Murguialday A, Birbaumer N, Hoffmann U, Luft A. Neurophysiology of robot-mediated training and therapy: a perspective for future use in clinical populations. Front Neurol. 2013;4:184. Published 2013 Nov 13. doi:10.3389/fneur.2013.00184

Originally published on 25.2.2020

via Technology: Filling the gaps in occupational and physical therapy – Hocoma

, , ,

Leave a comment

[Abstract] A soft robotic glove for hand rehabilitation training controlled by movements of the healthy hand – Full Text PDF

Abstract

Most stroke patients with hand dysfunction have normal function of one side of the body and their intact musculoskeletal systems are intact. Their hand function can be recovered through rehabilitation training. In this paper, a 3D-printed pneumatic-driven soft robotic glove is designed for hand rehabilitation training controlled by the movements of the healthy hand. Data glove is used to collect the motion data of the healthy hand that is then used to control the robotic glove. Characterization tests of the glove were carried out to prove the feasibility of the soft robotic glove. The experimental results show that the robotic glove can assist users to complete the rehabilitation training task.

via (PDF) A soft robotic glove for hand rehabilitation training controlled by movements of the healthy hand

, , , , , ,

Leave a comment

[ARTICLE] Brain-Machine Neurofeedback: Robotics or Electrical Stimulation? – Full Text

Neurotechnology such as brain-machine interfaces (BMI) are currently being investigated as training devices for neurorehabilitation, when active movements are no longer possible. When the hand is paralyzed following a stroke for example, a robotic orthosis, functional electrical stimulation (FES) or their combination may provide movement assistance; i.e., the corresponding sensory and proprioceptive neurofeedback is given contingent to the movement intention or imagination, thereby closing the sensorimotor loop. Controlling these devices may be challenging or even frustrating. Direct comparisons between these two feedback modalities (robotics vs. FES) with regard to the workload they pose for the user are, however, missing. Twenty healthy subjects controlled a BMI by kinesthetic motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the EEG beta frequency-band (17–21 Hz) was turned into passive opening of the contralateral hand by a robotic orthosis or FES in a randomized, cross-over block design. Mental demand, physical demand, temporal demand, performance, effort, and frustration level were captured with the NASA Task Load Index (NASA-TLX) questionnaire by comparing these workload components to each other (weights), evaluating them individually (ratings), and estimating the respective combinations (adjusted workload ratings). The findings were compared to the task-related aspects of active hand movement with EMG feedback. Furthermore, both feedback modalities were compared with regard to their BMI performance. Robotic and FES feedback had similar workloads when weighting and rating the different components. For both robotics and FES, mental demand was the most relevant component, and higher than during active movement with EMG feedback. The FES task led to significantly more physical (p = 0.0368) and less temporal demand (p = 0.0403) than the robotic task in the adjusted workload ratings. Notably, the FES task showed a physical demand 2.67 times closer to the EMG task, but a mental demand 6.79 times closer to the robotic task. On average, significantly more onsets were reached during the robotic as compared to the FES task (17.22 onsets, SD = 3.02 vs. 16.46, SD = 2.94 out of 20 opportunities; p = 0.016), even though there were no significant differences between the BMI classification accuracies of the conditions (p = 0.806; CI = −0.027 to −0.034). These findings may inform the design of neurorehabilitation interfaces toward human-centered hardware for a more natural bidirectional interaction and acceptance by the user.

Introduction

About half of all severely affected stroke survivors remain with persistent motor deficits in the chronic disease stage despite therapeutic interventions on the basis of the current standard of care (Winters et al., 2015). Since these patients cannot use the affected hand for activities of daily living, novel interventions investigate different neurotechnological devices to facilitate upper limb motor rehabilitation, such as brain-machine interfaces (BMI), robotic orthoses, neuromuscular functional electrical stimulation (FES), and brain stimulation (Coscia et al., 2019). BMI approaches, for example, aim at closing the impaired sensorimotor loop in severe chronic stroke patients. They use robotic orthoses (Ang et al., 2015Kasashima-Shindo et al., 2015Belardinelli et al., 2017), FES devices (Kim et al., 2016Biasiucci et al., 2018), and their combination (Grimm et al., 2016cResquín et al., 2017) to provide natural sensory and proprioceptive neurofeedback during movement intention or imagery. It is hypothesized that this approach will lead to reorganization of the corticospinal network through repetitive practice, and might ultimately restore the lost motor function (Naros and Gharabaghi, 20152017Belardinelli et al., 2017Guggenberger et al., 2018).

However, these novel approaches often result in no relevant clinical improvements in severe chronic stroke patients yet (Coscia et al., 2019). Therefore, recent research has taken a refined and rather mechanistic approach, e.g., by targeting physiologically grounded and clinically relevant biomarkers with BMI neurofeedback; this has led to the conceptional differentiation between restorative therapeutic BMIs on the one side (as those applied in this study) and classical assistive BMIs on the other side like those applied to control devices such as wheel-chairs (Gharabaghi, 2016): While assistive BMIs intend to maximize the decoding accuracy, restorative BMIs want to enhance behaviorally relevant biomarkers. Specifically, brain oscillations in the beta frequency band have been suggested as potential candidate markers and therapeutic targets for technology-assisted stroke rehabilitation with restorative BMIs (Naros and Gharabaghi, 20152017Belardinelli et al., 2017), since they are known to enhance signal propagation in the motor system and to determine the input-output ratio of corticospinal excitability in a frequency- and phase-specific way (Raco et al., 2016Khademi et al., 20182019Naros et al., 2019).

However, these restorative BMI devices differ from their predecessors, i.e., assistive BMIs, by an intentionally regularized and restricted feature space, e.g., by using the beta frequency band as a feedback signal for BMI control (Gharabaghi, 2016Bauer and Gharabaghi, 2017). Such a more specific approach is inherently different from previous more flexible algorithms that select and weight brain signal features to maximize the decoding accuracy of the applied technology; restorative BMIs like the those applied in this study have, therefore, relevantly less classification accuracy than classical assistive BMIs (Vidaurre et al., 2011Bryan et al., 2013). As the regularized and restricted feature space of such restorative BMI devices leads to a lower classification accuracy in comparison to more flexible approaches, it may be frustrating even for healthy participants (Fels et al., 2015). IN the context of the present study, we conjectured that such challenging tasks will increase the relevance of extraneous load aspects like the workload (Schnotz and Kürschner, 2007). Furthermore, the modulation range of the oscillatory beta frequency band is compromised in stroke patients, proportionally to their motor impairment level (Rossiter et al., 2014Shiner et al., 2015). That means that more severely affected patients show less oscillatory event-related desynchronization (ERD) and synchronization (ERS) during motor execution or imagery (Pfurtscheller and Lopes da Silva, 1999). To our understanding, this underlines the relevance of beta oscillations as a therapeutic target for post-stroke rehabilitation. At the same time, however, this poses a major challenge for the affected patients and may, thereby, compromise their therapeutic benefit (Gomez-Rodriguez et al., 2011a,bBrauchle et al., 2015).

To overcome these hurdles that are inherent to restorative BMI devices, we have investigated different approaches in the past: (i) Reducing the brain signal attenuation by the skull via the application of epidural interfaces (Gharabaghi et al., 2014b,cSpüler et al., 2014), (ii) Augmenting the afferent feedback of the robotic orthosis by providing concurrent virtual reality input (Grimm et al., 2016a,b), (iii) combining the orthosis-assisted movements with neuromuscular (Grimm and Gharabaghi, 2016Grimm et al., 2016c) or transcranial electrical stimulation (Naros et al., 2016a) to enhance the cortical modulation range (Reynolds et al., 2015), and (iv) optimizing the mental workload related to the use of BMI devices.

In this study, we focus on the latter approach, i.e., optimizing the mental workload related to the use of BMI devices. For the latter approach it would be necessary to better understand the workloads related to different technologies applied in the context of BMI feedback (robotics vs. FES). We, therefore, investigated the mental demand, physical demand, temporal demand, performance, effort, and frustration of healthy subjects controlling a BMI by motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the beta frequency-band was turned into passive opening of the contralateral hand by a robotic exoskeleton or FES in a randomized, cross-over block design. The respective workloads were compared to the task-related aspects of active hand movement with EMG feedback. We conjectured a feedback-specific workload profile that would be informative for more personalized future BMI approaches.

Methods

Subjects

We recruited 20 healthy subjects (age = 23.5 ± 1.08 yeas [mean ± SD], range 19–27, 15 female) for this study. Subjects were not naive to the tasks. All were right-handed and reached a score equal or above 60 in the Edinburgh Handedness Inventory (Oldfield, 1971). The subjects gave their written informed consent before participation and the study protocol was approved by the Ethics Committee of the Medical Faculty of the University of Tübingen. They received monetary compensation.

Subject Preparation

We used Ag/AgCl electrodes in a 32 channel setup according to the international 10-20 system (Fp1, Fp2, F3, Fz, F4, FC5, FC3, FC1, FCz, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, TP9, CP5, CP3, CP1, CPz, CP2, CP4, CP6, P3, Pz, P4, O1, O2 with TP10 as Reference and AFz as Ground) to examine the cortical activation pattern during the training session. Electrode impedances were set below 10 kΩ. All signals are digitalized at a sampling frequency of 1,000 Hz (robotic block) or 5,000 Hz (FES block) using Brain Products Amplifiers and transmitted online to BCI2000 software. BCI2000 controlled in combination with a custom-made software the respective feedback device, i.e., either the robotic orthosis or the functional electrical stimulation. Depending on the task, one of the following preparations was performed. Either the robotic hand orthosis (Amadeo, Tyromotion) was attached to the subject’s left hand (Figure 1A), fixated with Velcro strips across the forearm and with magnetic pads on the fingertips (Gharabaghi et al., 2014aNaros et al., 2016b); or functional electrical stimulation (FES, Figure 1B) was applied to the M. extensor digitorum communis (EDC) by the RehaMove2 (Hasomed GmbH, Magdeburg) with two self-adhering electrodes (50 mm, HAN-SEN Trading & Consulting GmbH, Hamburg). First an electrode was fixed to the distal end of the EDC’s muscle belly serving as ground. Then a rectangular electrode prepared with contact gel was used to find the optimal place for the second electrode where maximal extension of the left hand could be achieved. Here a custom written Matlab script was executed to detect the current threshold needed for the extension. Starting at 1 mA, the current was increased in steps of 0.5–1 mA. During each trial, FES was applied for 3 s with a pulse width of 1,000 μs and a frequency of 100 Hz. At the beginning of stimulation, a ramping protocol was implemented for 500 ms. Once, the correct position and threshold of stimulation were found, the temporary electrode was replaced by the second stimulation electrode and both were fixed with tape. A mean stimulation intensity of 6.5 mA (SD = 2.27) was required to cause the desired contraction in this study.

Figure 1. Experimental set-up. (Left) Robotic hand orthosis as feedback device (Amadeo, Tyromotion GmbH, Graz). (Middle) Neuromuscular forearm stimulation as feedback device (RehaMove 2, Hasomed GmbH, Magdeburg). In both cases, a brain-machine interface (BMI) detected motor imagery-related oscillations in the beta frequency band by an electroencephalogram (EEG) and provided via a BCI2000-system contingent feedback by moving the hand with either the robot or the electrical stimulation. (Right) The EEG montage used in this study.

[…]

Continue —-> Frontiers | Brain-Machine Neurofeedback: Robotics or Electrical Stimulation? | Bioengineering and Biotechnology

, , , , , , , ,

Leave a comment

[Abstract] Robot-assisted therapy for arm recovery for stroke patients: state of the art and clinical implication

Abstract

Introduction: Robot-assisted therapy is an emerging approach that performs highly repetitive, intensive, task oriented and quantifiable neuro-rehabilitation. In the last decades, it has been increasingly used in a wide range of neurological central nervous system conditions implying an upper limb paresis. Results from the studies are controversial, for the many types of robots and their features often not accompanied by specific clinical indications about the target functions, fundamental for the individualized neurorehabilitation program.

Areas covered: This article reviews the state of the art and perspectives of robotics in post-stroke rehabilitation for upper limb recovery. Classifications and features of robots have been reported in accordance with technological and clinical contents, together with the definition of determinants specific for each patient, that could modify the efficacy of robotic treatments. The possibility of combining robotic intervention with other therapies has also been discussed.

Expert commentary: The recent wide diffusion of robots in neurorehabilitation has generated a confusion due to the commingling of technical and clinical aspects not previously clarified. Our critical review provides a possible hypothesis about how to match a robot with subject’s upper limb functional abilities, but also highlights the need of organizing a clinical consensus conference about the robotic therapy.

Article Highlights

Robotic neurorehabilitation has the potential to improve the quality and intensity of rehabilitation treatments in order to promote motor-cognitive recovery following a central nervous system disease.

Controversial results in literature maybe generated by confusion in the use of robots related to many technological and clinical features, and emphasized by excessive optimism or scepticism about this technology.

Budgets spent for robots in rehabilitation are expected to grow dramatically in the next future, but there is the need of evidence-based proofs to balance the business push.

There is need of further researches in motor-cognitive technological rehabilitation in order to better understand the gain that robotic therapy could add to conventional therapy in relation to the patient’s cognitive reserve.

There is a need for clinical consensus conferences that might give clinical indication to end users.
via Robot-assisted therapy for arm recovery for stroke patients: state of the art and clinical implication: Expert Review of Medical Devices: Vol 0, No 0

, , , , , , , , , , , ,

Leave a comment

[ARTICLE] Increased gait variability during robot-assisted walking is accompanied by increased sensorimotor brain activity in healthy people – Full Text

Abstract

Background

Gait disorders are major symptoms of neurological diseases affecting the quality of life. Interventions that restore walking and allow patients to maintain safe and independent mobility are essential. Robot-assisted gait training (RAGT) proved to be a promising treatment for restoring and improving the ability to walk. Due to heterogenuous study designs and fragmentary knowlegde about the neural correlates associated with RAGT and the relation to motor recovery, guidelines for an individually optimized therapy can hardly be derived. To optimize robotic rehabilitation, it is crucial to understand how robotic assistance affect locomotor control and its underlying brain activity. Thus, this study aimed to investigate the effects of robotic assistance (RA) during treadmill walking (TW) on cortical activity and the relationship between RA-related changes of cortical activity and biomechanical gait characteristics.

Methods

Twelve healthy, right-handed volunteers (9 females; M = 25 ± 4 years) performed unassisted walking (UAW) and robot-assisted walking (RAW) trials on a treadmill, at 2.8 km/h, in a randomized, within-subject design. Ground reaction forces (GRFs) provided information regarding the individual gait patterns, while brain activity was examined by measuring cerebral hemodynamic changes in brain regions associated with the cortical locomotor network, including the sensorimotor cortex (SMC), premotor cortex (PMC) and supplementary motor area (SMA), using functional near-infrared spectroscopy (fNIRS).

Results

A statistically significant increase in brain activity was observed in the SMC compared with the PMC and SMA (p < 0.05), and a classical double bump in the vertical GRF was observed during both UAW and RAW throughout the stance phase. However, intraindividual gait variability increased significantly with RA and was correlated with increased brain activity in the SMC (p = 0.05; r = 0.57).

Conclusions

On the one hand, robotic guidance could generate sensory feedback that promotes active participation, leading to increased gait variability and somatosensory brain activity. On the other hand, changes in brain activity and biomechanical gait characteristics may also be due to the sensory feedback of the robot, which disrupts the cortical network of automated walking in healthy individuals. More comprehensive neurophysiological studies both in laboratory and in clinical settings are necessary to investigate the entire brain network associated with RAW.

Background

Safe and independent locomotion represents a fundamental motor function for humans that is essential for self-contained living and good quality of life [1,2,3,4,5]. Locomotion requires the ability to coordinate a number of different muscles acting on different joints [6,7,8], which are guided by cortical and subcortical brain structures within the locomotor network [9]. Structural and functional changes within the locomotor network are often accompanied by gait and balance impairments which are frequently considered to be the most significant concerns in individuals suffering from brain injuries or neurological diseases [51011]. Reduced walking speeds and step lengths [12] as well as non-optimal amount of gait variability [13,14,15] are common symptoms associated with gait impairments that increase the risk of falling [16].

In addition to manual-assisted therapy, robotic neurorehabilitation has often been applied in recent years [1718] because it provides early, intensive, task-specific and multi-sensory training which is thought to be effective for balance and gait recovery [171920]. Depending on the severity of the disease, movements can be completely guided or assisted, tailored to individual needs [17], using either stationary robotic systems or wearable powered exoskeletons.

Previous studies investigated the effectiveness of robot-assisted gait training (RAGT) in patients suffering from stroke [2122], multiple sclerosis [23,24,25,26], Parkinson’s disease [2728], traumatic brain injury [29] or spinal cord injury [30,31,32]. Positive effects of RAGT on walking speed [3334], leg muscle force [23] step length, and gait symmetry [2935] were reported. However, the results of different studies are difficult to summarize due to the lack of consistency in protocols and settings of robotic-assisted treatments (e.g., amount and frequency of training sessions, amount and type of provided robotic support) as well as fragmentary knowledge of the effects on functional brain reorganization, motor recovery and their relation [3637]. Therefore, it is currently a huge challenge to draw guidelines for robotic rehabilitation protocols [2236,37,38]. To design prologned personalized training protocols in robotic rehabilitation to maximize individual treatment effects [37], it is crucial to increase the understanding of changes in locomotor patterns [39] and brain signals [40] underlying RAGT and how they are related [3641].

A series of studies investigated the effects of robotic assistance (RA) on biomechanical gait patterns in healthy people [3942,43,44]. On one side, altered gait patterns were reported during robot-assisted walking (RAW) compared to unassisted walking (UAW), in particular, substantially higher muscle activity in the quadriceps, gluteus and adductor longus leg muscles and lower muscle activity in the gastrocnemius and tibialis anterior ankle muscles [3942] as well as reduced  lower-body joint angles due to the little medial-lateral hip movements [45,46,47]. On the other side, similar muscle activation patterns were observed during RAW compared to UAW [444849], indicating that robotic devices allow physiological muscle activation patterns during gait [48]. However, it is hypothesized that the ability to execute a physiological gait pattern depends on how the training parameters such as body weight support (BWS), guidance force (GF) or kinematic restrictions in the robotic devices are set [444850]. For example, Aurich-Schuler et al. [48] reported that the movements of the trunk and pelvis are more similar to UAW on a treadmill when the pelvis is not fixed during RAW, indicating that differences in musle activity and kinematic gait characteristics between RAW and UAW are due to the reduction in degrees of freedom that user’s experience while walking in the robotic device [45]. In line with this, a clinical concern that is often raised with respect to RAW is the lack of gait variability [454850]. It is assumed that since the robotic systems are often operated with 100% GF, which means that the devices attempt to force a particular gait pattern regardless of the user’s intentions, the user lacks the ability to vary and adapt his gait patterns [45]. Contrary to this, Hidler et al. [45] observed differences in kinematic gait patterns between subsequent steps during RAW, as demonstrated by variability in relative knee and hip movements. Nevertheless, Gizzi et al. [49] showed that the muscular activity during RAW was clearly more stereotyped and similar among individuals compared to UAW. They concluded that RAW provides a therapeutic approach to restore and improve walking that is more repeatable and standardized than approaches based on exercising during UAW [49].

In addition to biomechanical gait changes, insights into brain activity and intervention-related changes in brain activity that relate to gait responses, will contribute to the optimization of therapy interventions [4151]. Whereas the application of functional magnetic resonance imaging (fMRI), considered as gold standard for the assessment of activity in cortical and subcortical structures, is restricted due to the vulnerability for movement artifacts and the range of motion in the scanner [52], functional near infrared spectroscopy (fNIRS) is affordable and easily implementable in a portable system, less susceptible to motion artifacts, thus facilitation a wider range of application with special cohorts (e.g., children, patients) and in everyday environments (e.g., during a therapeutic session of RAW or UAW) [5354]. Although with lower resolution compared to fMRI [55], fNIRS also relies on the principle of neurovascular coupling and allows the indirect evaluation of cortical activation [5657] based on hemodynamic changes which are analogous to the blood-oxygenation-level-dependent responses measured by fMRI [56]. Despite limited depth sensitivity, which restricts the measurement of brain activity to cortical layers, it is a promising tool to investigate the contribution of cortical areas to the neuromotor control of gross motor skills, such as walking [53]. Regarding the cortical correlates of walking, numerous studies identified either increaesed oxygenated hemoglobin (Hboxy) concentration changes in the sensorimotor cortex (SMC) by using fNIRS [5357,58,59] or suppressed alpha and beta power in sensorimotor areas by using electroencephalography (EEG) [60,61,62] demonstrating that motor cortex and corticospinal tract contribute directly to the muscle activity of locomotion [63]. However, brain activity during RAW [366164,65,66,67,68], especially in patients [6970] or by using fNIRS [6869], is rarely studied [71].

Analyzing the effects of RA on brain activity in healthy volunteers, Knaepen et al. [36] reported significantly suppressed alpha and beta rhythms in the right sensory cortex during UAW compared to RAW with 100% GF and 0% BWS. Thus, significantly larger involvement of the SMC during UAW compared to RAW were concluded [36]. In contrast, increases of Hboxy were observed in motor areas during RAW compared UAW, leading to the conclusion that RA facilitated increased cortical activation within locomotor control systems [68]. Furthermore, Simis et al. [69] demonstrated the feasibility of fNIRS to evaluate the real-time activation of the primary motor cortex (M1) in both hemispheres during RAW in patients suffering from spinal cord injury. Two out of three patients exhibited enhanced M1 activation during RAW compared with standing which indicate the enhanced involvement of motor cortical areas in walking with RA [69].

To summarize, previous studies mostly focused the effects of RA on either gait characteristics or brain activity. Combined measurements investigating the effects of RA on both biomechanical and hemodynamic patterns might help for a better understanding of the neurophysiological mechanisms underlying gait and gait disorders as well as the effectiveness of robotic rehabilitation on motor recovery [3771]. Up to now, no consensus exists regarding how robotic devices should be designed, controlled or adjusted (i.e., device settings, such as the level of support) for synergistic interactions with the human body to achieve optimal neurorehabilitation [3772]. Therefore, further research concerning behavioral and neurophysiological mechanisms underlying RAW as well as the modulatory effect of RAGT on neuroplasticy and gait recovery are required giving the fact that such knowledge is of clinical relevance for the development of gait rehabilitation strategies.

Consequently, the central purpose of this study was to investigate both gait characteristics and hemodynamic activity during RAW to identify RAW-related changes in brain activity and their relationship to gait responses. Assuming that sensorimotor areas play a pivotal role within the cortical network of automatic gait [953] and that RA affects gait and brain patterns in young, healthy volunteers [39424568], we hypothesized that RA result in both altered gait and brain activity patterns. Based on previous studies, more stereotypical gait characteristics with less inter- and intraindividual variability are expected during RAW due to 100% GF and the fixed pelvis compared to UAW [4548], wheares brain activity in SMC can be either decreased [36] or increased [68].

Methods

This study was performed in accordance with the Declaration of Helsinki. Experimental procedures were performed in accordance with the recommendations of the Deutsche Gesellschaft für Psychologie and were approved by the ethical committee of the Medical Association Hessen in Frankfurt (Germany). The participants were informed about all relevant study-related contents and gave their written consent prior to the initiation of the experiment.

Participants

Twelve healthy subjects (9 female, 3 male; aged 25 ± 4 years), without any gait pathologies and free of extremity injuries, were recruited to participate in this study. All participants were right-handed, according to the Edinburg handedness-scale [73], without any neurological or psychological disorders and with normal or corrected-to-normal vision. All participants were requested to disclose pre-existing neurological and psychological conditions, medical conditions, drug intake, and alcohol or caffeine intake during the preceding week.

Experimental equipment

The Lokomat (Hocoma AG, Volketswil, Switzerland) is a robotic gait-orthosis, consisting of a motorized treadmill and a BWS system. Two robotic actuators can guide the knee and hip joints of participants to match pre-programmed gait patterns, which were derived from average joint trajectories of healthy walkers, using a GF ranging from 0 to 100% [7475] (Fig. 1a). Kinematic trajectories can be adjusted to each individual’s size and step preferences [45]. The BWS was adjusted to 30% body weight for each participant, and the control mode was set to provide 100% guidance [64].

figure1

Montage and Setup. a Participant during robot-assisted walking (RAW), with functional near-infrared spectroscopy (fNIRS) montage. b fNIRS montage; S = Sources; D = Detectors c Classification of regions of interest (ROI): supplementary motor area/premotor cortex  (SMA/PMC) and sensorimotor cortex (SMC) 

[…]

Continue —-> Increased gait variability during robot-assisted walking is accompanied by increased sensorimotor brain activity in healthy people | Journal of NeuroEngineering and Rehabilitation | Full Text

, , , , , , , , ,

Leave a comment

[BOOK] Intelligent Biomechatronics in Neurorehabilitation – Xiaoling Hu – Google Books

Front Cover
Academic PressOct 19, 2019 – Science – 286 pages

Intelligent Biomechatronics in Neurorehabilitation presents global research and advancements in intelligent biomechatronics and its applications in neurorehabilitation. The book covers our current understanding of coding mechanisms in the nervous system, from the cellular level, to the system level in the design of biological and robotic interfaces. Developed biomechatronic systems are introduced as successful examples to illustrate the fundamental engineering principles in the design. The third part of the book covers the clinical performance of biomechatronic systems in trial studies. Finally, the book introduces achievements in the field and discusses commercialization and clinical challenges.

As the aging population continues to grow, healthcare providers are faced with the challenge of developing long-term rehabilitation for neurological disorders, such as stroke, Alzheimer’s and Parkinson’s diseases. Intelligent biomechatronics provide a seamless interface and real-time interactions with a biological system and the external environment, making them key to automation services.

  • Written by international experts in the rehabilitation and bioinstrumentation industries
  • Covers the current understanding of nervous system coding mechanisms, which are the basis for biological and robotic interfaces
  • Demonstrates and discusses robotic rehabilitation effectiveness and automatic evaluation

via Intelligent Biomechatronics in Neurorehabilitation – Xiaoling Hu – Google Books

, , , , ,

Leave a comment

[Abstract + References] An Exoskeleton Design Robotic Assisted Rehabilitation: Wrist & Forearm – Conference paper

Abstract

Robotic systems are being used in physiotherapy for medical purposes. Providing physical training (therapy) is one of the main applications of fields of rehabilitation robotics. Upper-extremity rehabilitation involves shoulder, elbow, wrist and fingers’ actions that stimulate patients’ independence and quality of life. An exoskeleton for human wrist and forearm rehabilitation is designed and manufactured. It has three degrees of freedom which must be fitted to real human wrist and forearm. Anatomical motion range of human limbs is taken into account during design. A six DOF Denso robot is adapted. An exoskeleton driven by a serial robot has not been come across in the literature. It is feasible to apply torques to specific joints of the wrist by this way. Studies are still continuing in the subject.

References

  1. 1.
    Krebs, H., Hogan, N., Aisen, M., Volpe, B.: Robot-aided neurorehabilitation, IEEE Transactions on Rehabilitation Engineering 6(1), 75–87 (1998).CrossRefGoogle Scholar
  2. 2.
    Lum, P. S., Burgar, C. G., Shor, P. C., Majmundar, M., Van Der Loos, M.: Robot-ass. movement training compared with conventional therapy techniques for the rehab. of upper limb motor function after stroke. Arc. of Phy. Med. and Rehab. 83(7), 952–959 (2002).Google Scholar
  3. 3.
    Gupta, A., O’Malley, M. K.: Design of a haptic arm exoskeleton for training and rehabilitation’’, IEEE/ASME Trans. on Mechatronics 11(3), 280–289 (2006).CrossRefGoogle Scholar
  4. 4.
    Nef, T., Mihelj, M., Kiefer, G., Perndl, C., Muller, R., Riener R.:Armin-Exoskeleton for arm therapy in stroke patients’’, In: 10th Int Conf. on Rehab Robotics, pp: 68–74 (2008).Google Scholar
  5. 5.
    Perry, J. C., Rosen, J., Burns, S.: Upper-limb powered exoskeleton design. IEEE/ASME Transactions on Mechatronics 12(4), 408–417 (2007).CrossRefGoogle Scholar
  6. 7.
    Otten, A., Voort, C., Stienen, A., Aarts, R., Van Asseldonk, E., Van der Kooij, H.: LIMPACT: a hydraulically powered self-aligning upper limb exoskeleton, IEEE/ASME Trans. on Mechatronics 20 (5), 2285–2298 (2015).CrossRefGoogle Scholar
  7. 8.
    Lum ,P. S., Burgar, C. G., Van Der Loos, M., Shor, P. C., Majmundar ,M., Yap, R..:The MIME robotic system for upper-limb neuro-rehabilitation: results from a clinical trial in subacute stroke, In: IEEE 9th Int. Conf.on Rehabilitation Robotics, pp: 511–514. (2005).Google Scholar
  8. 9.
    Khor, K. X., Chin, P. J. H., Hisyam ,A. R., Yeong, C. F., Narayanan A. L. T., Su,E. L. M.: development of cr2-haptic: a compact and portable rehabilitation robot for wrist and forearm training, In: IEEE Conf. on Biomedical Eng. and Sciences, pp: 424–429 (2014).Google Scholar
  9. 10.
    Oblak, J., Cikajlo, I., Matjacic, Z.:Universal haptic drive: a robot for arm and wrist rehabilitation. IEEE Tr. on Neural Sys. and Rehab. Eng.18(3), 293–302 (2010).CrossRefGoogle Scholar
  10. 11.
    Lum, S., Reinkensmeyer, D., Lehman, S.: Robotic assist devices for bimanual physical therapy: preliminary experiments. IEEE Trans. on Rehab. Eng. 1(3), 185–191, (1993).CrossRefGoogle Scholar
  11. 12.
    Allington, J., Spencer, S. J., Klein, J., Buell, M., Reinkensmeyer, D. J., Bobrow, J.:Supinator Extender (sue): A pneumatically actuated robot for forearm/wrist rehabilitation after stroke. In: EMBC, 2011, pp:1579–1582 (2011).Google Scholar
  12. 13.
    Gupta, A., O’Malley, M. K., Patoglu, V., Burgar, C., :Design, control and performance of ricewrist: a force feedback wrist exoskeleton for rehabilitation and training, The Int. J. of Robotics Research 27 (2), 233–251 (2008).Google Scholar
  13. 14.
    Spencer, S. J., Klein, J., Minakata, K., Le, V., Bobrow, J. E., Reinkensmeyer, D. J.,: A low cost parallel robot and trajectory optimization method for wrist and forearm rehabilitation. In:(BioRob 2008), pp: 869–874 (2008).Google Scholar
  14. 15.
    Pehlivan, A., Lee, S., O’Malley, M.: Mechanical design of Ricewrist-s: a WF exoskeleton for stroke and spinal cord injury rehabilitation. In: BIOROB, pp: 1573–1578, (2012).Google Scholar
  15. 16.
    Martinez, J. F., Ng, P., Lu, S., Campagna, M., Celik, O.: Design of wrist gimbal: a forearm and wrist exoskeleton for stroke rehabilitation. In: ICORR, pp: 1–6 (2013).Google Scholar
  16. 17.
    Beekhuis, J. H., Westerveld, A. J., Van der Kooij, H., Stienen, A. H. A.: Design of a self-aligning 3-dof actuated exoskeleton for diagnosis and training of wrist and forearm after stroke, IEEE Int. Conf. on Rehabilitation Robotics, (2013).Google Scholar
  17. 18.
    Omarkulov, N., Telegenov, K., Zeinnullin, M., Tursynbek, I.: Preliminary mechanical design of nu-wrist: a 3 dof self-aligning wrist rehabilitation robot. In: BIOROB, 2016.Google Scholar
  18. 19.
    Dağdelen, M., Sarıgeçili M. İ.: Development of a conceptual model for w/f rehabilitation robot with two degrees of freedom, Advances in Robot Design and Int. Control (2017).Google Scholar
  19. 20.
    Kütük, M. E., Daş M. T., Dülger, L. C.: Forward and inverse kinematic analysis of denso robot. In: AzCIFToMM (2017).Google Scholar
  20. 21.
  21. 22.

Image result for An Exoskeleton Design Robotic Assisted Rehabilitation: Wrist & Forearm

Fig. 1. Wrist and forearm motions [17]

via An Exoskeleton Design Robotic Assisted Rehabilitation: Wrist &amp; Forearm | SpringerLink

 

, , , , , , , ,

Leave a comment

[WEB SITE] European Rehabilitation Robotics School

Who We Are

SCHOOL CONCEPT AND VISION

The wide spectrum of employment in PRM (Physical and Rehabilitation Medicine) of Robotics and New Technologies is a concrete reality, but PRM training Centres offering proper programs are sparse at national and international level and skills needed to appropriately apply robotics are usually achieved “on the field” by rehabilitation professionals without any prior specific education.
The inter-professional cooperation, so strongly needed in research and clinical activities, is very weak in Health facilities and between medical professionals with engineers and other ICT experts.
The actual gap is mostly educational and there is a great need to enhance training and knowledges for PRM physicians (and the same for Physiotherapists, Speech Therapists, Occupational Therapists, Orthotics and Engineers).

 

European Society of PRM (ESPRM) promotes, through Scientific Committe on Robotics and in cooperation UEMS PRM Section and Board an innovative approach based on a summer annual School (Robotic Rehabilitation Summer School-R2S2) with the main goals to enhance scientific information and foster education in Robotics applications.
The educational programme is developed in the frame of the theoretical knowledge and evidence-based approach provided by recent researches indications and international publications, while exploiting the technical support of IISART and other Companies which carry out research and productions in this field in connection with growing technical and clinical experiences realized all over the Europe.

What We Do

SCHOOL WEBSITE MISSION

 

The purpose of the School is to harmonize and increase the level of knowledge concerning the use of robotics in rehabilitation, for PRM physicians (and if possible all rehabilitation professionals) to enhance collaboration, communication and sharing, both on a clinical and research basis.
Students will be able at the end of the Sessions to plan and manage routinely and daily therapies integrating robotics and new technologies; they will have the basis for financial or organizational issues for such therapies and, finally, they will be able to design and realize proper research trials aimed at assessing the efficacy, effectiveness and efficiency of robotic rehabilitation. School Courses are open to European PRM physicians and students.

It is a fundamental tool to maintain and increase all over the year and places the activity:

Before School/On Line

Educational material (slides packages, webinars etc) will be available on-line from 2 months before practical session for all enrolled students.

Post School/On-Line

All educational materials will be available online for all students; a “meet the experts” service will be available also for six months after the end of the School/On-Site.

Visit SITE —>  European Rehabilitation Robotics School

, , , ,

Leave a comment

%d bloggers like this: