Posts Tagged robotic rehabilitation

[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.

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[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.

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[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

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[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) 

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[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

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[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.

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Fig. 1. Wrist and forearm motions [17]

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

 

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[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

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[ARTICLE] Muscle fatigue assessment during robot-mediated movements – Full Text

Abstract

Background

Several neuromuscular disorders present muscle fatigue as a typical symptom. Therefore, a reliable method of fatigue assessment may be crucial for understanding how specific disease features evolve over time and for developing effective rehabilitation strategies. Unfortunately, despite its importance, a standardized, reliable and objective method for fatigue measurement is lacking in clinical practice and this work investigates a practical solution.

Methods

40 healthy young adults performed a haptic reaching task, while holding a robotic manipulandum. Subjects were required to perform wrist flexion and extension movements in a resistive visco-elastic force field, as many times as possible, until the measured muscles (mainly flexor and extensor carpi radialis) exhibited signs of fatigue. In order to analyze the behavior and the characteristics of the two muscles, subjects were divided into two groups: in the first group, the resistive force was applied by the robot only during flexion movements, whereas, in the second group, the force was applied only during extension movements. Surface electromyographic signals (sEMG) of both flexor and extensor carpi radialis were acquired. A novel indicator to define the Onset of Fatigue (OF) was proposed and evaluated from the Mean Frequency of the sEMG signal. Furthermore, as measure of the subjects’ effort throughout the task, the energy consumption was estimated.

Results

From the beginning to the end of the task, as expected, all the subjects showed a decrement in Mean Frequency of the muscle involved in movements resisting the force. For the OF indicator, subjects were consistent in terms of timing of fatigue; moreover, extensor and flexor muscles presented similar OF times. The metabolic analysis showed a very low level of energy consumption and, from the behavioral point of view, the test was well tolerated by the subjects.

Conclusion

The robot-aided assessment test proposed in this study, proved to be an easy to administer, fast and reliable method for objectively measuring muscular fatigue in a healthy population. This work developed a framework for an evaluation that can be deployed in a clinical practice with patients presenting neuromuscular disorders. Considering the low metabolic demand, the requested effort would likely be well tolerated by clinical populations.

Background

Muscle fatigue has been defined as “the failure to maintain a required or expected force” [1] and it is a complex phenomenon experienced in everyday life that has reached great interest in the areas of sports, medicine and ergonomics [2]. Muscle fatigue can affect task performance, posture-movement coordination [3], position sense [4] and it can be a highly debilitating symptom in several pathologies [5]. For many patients with neuromuscular impairments, taking into account muscle fatigue is of crucial importance in the design of correct rehabilitation protocols [6] and fatigue assessment can provide crucial information about skeletal muscle function. Specifically, several neuromuscular diseases (e.g. Duchenne, Becker Muscular Dystrophies, and spinal muscular atrophy) present muscle fatigue as a typical symptom [7], and fatigue itself accounts for a significant portion of the disease burden. A systematic approach to assess muscle fatigue might provide important cues on the disability itself, on its progression and on the efficacy of adopted therapies. In particular, therapeutic strategies are now under deep investigation and a lot of effort has been devoted to accelerate the development of drugs targeting these disorders [8]. Therefore, the need for an objective tool to measure muscle fatigue is impelling and of great relevance.

Currently, in clinical practice muscle fatigue is evaluated by means of qualitative rating scales like the 6-min walk test (6MWT) [9] or through subjective questionnaires administered to the patient (e.g. the Multidimensional Fatigue Inventory (MFI), the Fatigue Severity Scale (FSS), and the Visual Analog Scale (VAS)) [10]. During the 6MWT patients have to walk, as fast as possible, along a 25 meters linear course and repeat it as often as they can for 6 min: ‘fatigue’ is then defined as the difference between the distance covered in the sixth minute compared to the first. Obviously, such a measure is only applicable to ambulant patients and this is a strong limitation to clinical investigation because a patient may lose ambulatory ability during a clinical trial, resulting in lost ability to perform the primary clinical endpoint [11]. It should also be considered that neuromuscular patients, e.g. subjects with Duchenne Muscular Dystrophy, generally lose ambulation before 15 years of age [12], excluding a large part of the population from the measurement of fatigue through the 6MWT. Since neuromuscular patients often experience a progressive weakness also in the upper limb, reporting of muscle fatigue in this region is common. A fatigue assessment for upper limb muscles could be used to monitor patients across different stages of the disease. As for the questionnaires, the MFI is a 20 items scale designed to evaluate five dimensions of fatigue (general fatigue, physical fatigue, reduced motivation, reduced activity, and mental fatigue) [13]. Similarly, the FSS questionnaire contains nine statements that rate the severity of fatigue symptoms and the patient has to agree or disagree with them [14]. The VAS is even more general: the patient has to indicate on a 10 cm line ranging from “no fatigue” to “severe fatigue” the point that best describes his/her level of fatigue [15]. Despite the ease to administer, such subjective assessments of fatigue may not correlate with the actual severity or characteristics of fatigue, and may provide just qualitative information with low resolution, reliability and objectivity. Considering various levels of efficacy among the methods currently used in clinical practice, research should focus on the development of an assessment tool for muscle fatigue, that is easy and fast to administer, even to patients with a high level of impairment. Such a tool, should provide clear results, be easy to read and understand by a clinician, be reliable and objectively correlated with the physiology of the phenomenon.

In general, muscle fatigue can manifest from either central and/or peripheral mechanisms. Under controlled conditions, surface electromyography (sEMG) is a non-invasive and widely used technique to evaluate muscle fatigue [16]. Certain characteristics of the sEMG signal can be indicators of muscle fatigue. For example during sub-maximal tasks, muscle fatigue will present with decreases in muscle fiber conduction velocity and frequency and increases in amplitude of the sEMG signal [16]. The trend and rate of change will depend on the intensity of the task: generally, sEMG amplitude has been observed to increase during sub-maximal efforts and decrease during maximal efforts; further it has been reported that there is a significantly greater decline in the frequency content of the signal during maximal efforts compared to sub-maximal [17]. Accordingly, spectral (i.e. mean frequency) and amplitude parameters (i.e. Root Mean Square (RMS)) of the signals, can be used to measure muscle fatigue as extensively discussed in many widely acknowledged studies [161819], however, context of contraction type and intensity must be specified for proper interpretation. A significant problem with the majority of existing protocols is that they rely on quantifying maximal voluntary force loss, maximum voluntary muscle contraction (MVC) [182021] or high fatiguing dynamic tasks [1922] that cannot be reliably performed in clinical practice, especially in the case of pediatric subjects. Actually, previous works pointed out that not only the capacity to maintain MVC can be limited by a lack of cooperation [2324], but also, that sustaining a maximal force in isometric conditions longer than 30 s reduces subject’s motivation leading to unreliable results [25]. Besides, neuromuscular patients might have a high level of impairment and low residual muscular function thus making even more difficult, as well as dangerous for their muscles, sustaining high levels of effort or the execution of a true MVC. In order to overcome this issue, maximal muscle contractions can be elicited by magnetic [10] or electrical stimulation [26]. Although such procedures allow to bypass the problem mentioned above, these involve involuntary muscle activation and not physiological recruitment of motor units [24]; moreover, they can be uncomfortable for patients and can require advanced training, which makes them difficult to be included in clinical fatigue assessment protocols. As for the above mentioned problem with children motivation, work by Naughton et al. [27] showed that the test-retest coefficient of variation of fatigue index during a Wing-Gate test, significantly decreased when using a computerized feedback game linked to pedal cadence, suggesting that game-based procedures may ensure more consistent results in children assessment.

In recent years, the assessment of sensorimotor function has been deepened thanks to the introduction of innovative protocols administered through robotic devices [28293031]. These methods have the ambition to add meaningful information to the existing clinical scales and can be exploited as a basis for the implementation of a muscle fatigue assessment protocol. In order to fill the gap between the need of a quantitative clinical measurement protocol of muscle fatigue and the lack of an objective method which does not demand a high level of muscle activity, we propose a new method based on a robotic test, which is fast and easy to administer. Further, we decided to address the analysis of muscle fatigue on the upper limb as to provide a test suitable to assess patients from the beginning to the late stages of the disease, regardless of walking ability. Moreover, we focused on an isolated wrist flexion/extension tasks to assess wrist muscle fatigue. This ensured repeatability of the tests and prevented the adoption of compensatory movements or poor postures that may occur in multi-segmental tasks, involving the shoulder-elbow complex. In the present work, we tested the method on healthy subjects with the specific goal to evaluate when during the test the first meaningful symptoms of fatigue appaered and not how much subjects are fatigued at the end of the test. The most relevant and novel features of the proposed test include the ability to perform the test regardless of the subjects’ capability and strength, the objectivity and repeatability of the data it provides, and the simplicity and minimal time required to administer.[…]

 

Continue —->  Muscle fatigue assessment during robot-mediated movements | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1

Fig. 1 Experimental setup. Participant sitting on a chair with the forearm secured to the WRISTBOT while performing the wrist rotation reaching task. The visual targets of the reaching task are shown on a dedicated screen

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[ARTICLE] Feasibility of the UR5 Industrial Robot for Robotic Rehabilitation of the Upper Limbs After Stroke – Full Text PDF

Abstract

Robot-assisted therapy is an emerging form of
rehabilitation treatment for motor recovery of the upper limbs
after neurological injuries such as stroke or spinal cord injury.
Robotic rehabilitation devices have the potential to reduce the
physical strain put on therapists due to the high-effort oneto-one
interactions between the therapist and patient involving
repetitive high-intensity movements to restore arm and
hand functions. Numerous custom robotic devices have been
developed in recent years to aid in physical rehabilitation
of stroke patients, but most commercially available systems
are high-cost devices because of low production volumes and
high development costs. In this paper, we analyse the safety
and functionality of the UR5 collaborative industrial robot
from Universal Robots equipped with an external force/torque
sensor in a real-time control system for typical rehabilitation
exercises. The aim of the paper is to show that a new class
of general-purpose industrial robots designed for human-robot
collaboration may prove a viable alternative to custom designs.
Experiments show that robotic rehabilitation of the upper
limbs using a standard industrial robot manipulator UR5
may be feasible. Results have the potential to reduce costs
and complexity for robotic rehabilitation devices, and thus
make robotic rehabilitation more affordable as a high-quality
therapeutic treatment for more patients.

Full Text PDF

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[Thesis] Robotic rehabilitation of upper-limb after stroke – Implementation of rehabilitation control strategy on robotic manipulator – Full Text PDF

Abstract
Globally, stroke is one of the main causes of permanent neurological damage \cite{WHO}. Partial or total paralysis of the extremities is the most common complication, with paralysis in upper-limbs being the most prevalent. Efficient and available rehabilitation therapy is essential for the patient’s recovery process. Traditional physical therapy is a resource intensive and commonly used approach. Research on robotic rehabilitation aims to provide a viable rehabilitation tool.The main objective of this master thesis is to design and implement a safe real-time control system on an industrial six-axis manipulator with an external force/torque sensor for robotic upper-limb rehabilitation of stroke patients.The chosen approach is to implement control strategies directly in the tool frame of the manipulator. This is achieved by utilizing the UR5 from Universal Robot and the Mini45 F/T sensor from ATI Industrial Automation. Two verification test are chosen based on activities of daily life (ADL). The best low-level control strategy is achieved by indirect force and torque control through a joint velocity interface.The UR5 firmware operates with an unknown internal controller. An external controller is designed incrementally to investigate the unknown system dynamics and find the best possible low-level performance. Numerous safety mechanisms are added to the external controller. Four high-level control strategies are developed and implemented.Three main safety-related challenges with robotic rehabilitation are identified. Two of them are related to and solved by the external force/torque sensor. The third challenge is related to the self-collisions inside the workspace of the UR5 manipulator. This challenge is also applicable to all six-axis robot manipulators. The three challenges are analyzed and solved with a safety-oriented approach.The safety and functionality of the robotic rehabilitation system are experimentally verified. The behaviour of the rehabilitation modes is analyzed and discussed based on raw data and video recordings.The conclusion is that robotic upper-limb rehabilitation of stroke patients utilizing the UR5 manipulator and the Mini45 F/T sensor is safe and feasible.

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