Posts Tagged Electromyography

[Abstract] Myoelectric Sensing for Intent Detection and Assessment in Upper-Limb Robotic Rehabilitation – Thesis

Abstract

This thesis explores how surface electromyography (EMG) — the measurement of muscle force through voltage changes at the skin surface – can be of use to the field of upper-limb robotic rehabilitation. We focus on two main aspects: detecting human intention from measured muscle activity and assessing human motor coordination through synchronous muscle activations known as muscle synergies – each examples of the bidirectional communication found in tightly integrated human-robot interaction. EMG-based intent detection presents an opportunity to examine and promote human engagement at the neuromuscular level, enabling new protocols for intervention that could be combined with robotic rehabilitation, particularly for the most impaired of users. Meanwhile, the latest research in motor control proposes that natural, healthy human movement can be characterized by the presence of certain muscle synergies, and that the alteration of these synergies indicates a disruption, from neurological impairment or some other physical constraints, in natural movement. Wearable robotic devices are capable of altering muscle synergies, and though the mechanisms are not yet understood, a focus on altering muscle synergies is a promising new approach to neurorehabilitation. This thesis employs a robotic exoskeleton for the elbow and wrist joints designed for research in robotic rehabilitation of individuals with neurological impairments and now integrated with a myoelectric control interface. We first demonstrate the ability of a myoelectric interface to discern the user’s intended direction of motion in single-degree-of-freedom (DoF) and multi-DoF control modes with 10 able-bodied participants and 4 participants with incomplete cervical spinal cord injury (SCI). Predictive accuracy was high for able-bodied participants (averages over 99% for single-DoF and near 90% for multi-DoF), and performance in the SCI group was promising (averages ranging from 85% to 95% for single-DoF, and variable multi-DoF performance averaging around 60%), which is encouraging for the future use of myoelectric interfaces in robotic rehabilitation for SCI. Second, we explore the identification of synchronous muscle synergies in the muscles controlling the elbow and wrist, and the possible effects of robot-imposed task constraints on the neural constrains represented by synergy patterns. Our results indicate that constraining the unused degrees of freedom during a single-DoF movement inside the exoskeleton does not have a significant effect on the underlying muscle synergies in the task, and that methodological choices in muscle synergy analysis also do not have a large effect on the outcome. With all of these findings, we have achieved a deeper understanding of the value myoelectric sensing can bring to upper-limb robotic rehabilitation, and how much potential it has to advance the field toward greater accessibility to individuals of all levels of impairment.

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[Abstract] Advances in motion and electromyography based wearable technology for upper extremity function rehabilitation: A review

Study Design

Scoping review.

Introduction

With the recent advances in technologies, interactive wearable technologies including inertial motion sensors and e-textiles are emerging in the field of rehabilitation to monitor and provide feedback and therapy remotely.

Purpose of the Study

This review article focuses on inertial measurement unit motion sensor and e-textiles–based technologies and proposes approaches to augment these interactive wearable technologies.

Methods

We conducted a comprehensive search of relevant electronic databases (eg, PubMed, the Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, The Cochrane Central Register of Controlled Trial, and the Physiotherapy Evidence Database). The scoping review included all study designs.

Results

Currently, there are a numerous research groups and companies investigating inertial motion sensors and e-textiles–based interactive wearable technologies. However, translation of these technologies to the clinic would need further research to increase ease of use and improve clinical validity of the outcomes of these technologies.

Discussion

The current review discusses the limitations of the interactive wearable technologies such as, limited clinical utility, bulky equipment, difficulty in setting up equipment inertial motion sensors and e-textiles.

Conclusion

There is tremendous potential for interactive wearable technologies in rehabilitation. With the evolution of cloud computing, interactive wearable systems can remotely provide intervention and monitor patient progress using models of telerehabilitation. This will revolutionize the delivery of rehabilitation and make rehabilitation more accessible and affordable to millions of individuals.

via Advances in motion and electromyography based wearable technology for upper extremity function rehabilitation: A review – ScienceDirect

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[ARTICLE] Effects of bodyweight support and guidance force on muscle activation during Locomat walking in people with stroke: a cross-sectional study – Full Text

Abstract

Background

Locomat is a robotic exoskeleton providing guidance force and bodyweight support to facilitate intensive walking training for people with stroke. Although the Locomat has been reported to be effective in improving walking performance, the effects of training parameters on the neuromuscular control remain unclear. This study aimed to compare the muscle activities between Locomat walking and treadmill walking at a normal speed, as well as to investigate the effects of varying bodyweight support and guidance force on muscle activation patterns during Locomat walking in people with stroke.

Methods

A cross-sectional study design was employed. Participants first performed an unrestrained walking on a treadmill and then walked in the Locomat with different levels of bodyweight support (30% or 50%) and guidance force (40% or 70%) at the same speed (1.2 m/s). Surface electromyography (sEMG) of seven muscles of the affected leg was recorded. The sEMG envelope was time-normalised and averaged over gait cycles. Mean sEMG amplitude was then calculated by normalising the sEMG amplitude with respect to the peak amplitude during treadmill walking for statistical analysis. A series of Non-parametric test and post hoc analysis were performed with a significance level of 0.05.

Results

Fourteen participants with stroke were recruited at the Yangzhi Affiliated Rehabilitation Hospital of Tongji University (female n = 1; mean age 46.1 ± 11.1 years). Only the mean sEMG amplitude of vastus medialis oblique during Locomat walking (50% bodyweight support and 70% guidance force) was significantly lower than that during treadmill walking. Reducing both bodyweight and guidance increased muscle activity of gluteus medius and tibialis anterior. Activity of vastus medialis oblique muscle increased as bodyweight support reduced, while that of rectus femoris increased as guidance force decreased.

Conclusions

The effects of Locomat on reducing muscle activity in people with stroke were minimized when walking at a normal speed. Reducing bodyweight support and guidance force increased the activity of specific muscles during Locomat walking. Effects of bodyweight support, guidance force and speed should be taken into account when developing individualized Locomat training protocols for clients with stroke.

Introduction

Gait disturbance is one of the major consequences associated with stroke. Due to the impaired supraspinal control, the gait pattern post stroke is characterized as muscle weakness, spasticity, abnormal muscular amplitude and asymmetrical temporal ordering of muscle activity [12]. Impaired walking ability not only reduces the functional independency of stroke survivors, but also increases a series of risks, like fall [3,4,5]. The restoration of functional walking ability requires intensive training with a symmetrical gait pattern [6,7,8].

Various robot-assisted gait trainers, like Locomat, G-EO system Evolution and Gait Trainer, have been designed and implemented in gait rehabilitation for stroke patients [9,10,11,12,13,14,15]. These gait trainers enable a repetitive walking training with predefined normal gait pattern and largely reduce the physical demand of therapists [16]. Those robot-assisted gait trainers, like Locomat (Hocoma, Switzerland), can provide a range of adjustable functions, including bodyweight support (BWS), guidance force (GF) and walking speed, allowing clinicians to develop an individualised training protocol that best fits patient’s ability level [1718]. Locomat training, however, has been found to reduce muscle activities in both healthy individuals and people with stroke when compared to overground walking [1920]. For example, Coenen and colleagues [20] found that the application of BWS and GF significantly reduced activities of several muscles of affected leg in people with stroke. This feature of Locomat training is considered as a negative aspect of its clinical implication because voluntary contraction of muscles plays a key role in motor relearning [21]. In addition, the exoskeletons of Locomat limit the movement in sagittal plane and reduce the degree of freedom of pelvis which may lead to abnormal interaction between the leg and exoskeleton as well as abnormal muscle activity pattern [1022].

There is sufficient evidence showing that the Locomat training provided better improvement in terms of independent walking ability, walking speed, balance and disability than conventional physiotherapy to people with stroke [23,24,25,26,27,28]. There is also evidence that Locomat training significantly improved the duration of single stance phase, step length ratio on the paretic leg when walking on the ground [2930]. However, there are also studies showing that the Locomat was not superior to conventional therapy in people with stroke [93031]. Despite the heterogeneous features of participants, the difference in training parameters of Locomat may also contribute to the controversial results. In healthy participants, there is ample evidence that BWS or GF can affect the activation of specific muscles [1019203233]. There are also studies reporting significant interactions between BWS, GF and walking speed on voluntary control indicating that the mechanisms of those parameters are complex [32]. In a recent study, however, researchers reported that varying BWS and GF was not associated with changes of muscle activity in people with stroke, whereas increasing walk speed led to greater muscle activity [34]. Since the walking speeds used in previous studies were relatively low (0.56 m/s and 0.61 m/s respectively) [1920] and the increase of speed was associated with greater muscle activity [3235], it is of interest to investigate whether a higher walking speed would modulate the difference in muscle activity between Locomat walking and treadmill walking.

To further investigate the effects of BWS and GF on active muscle activity, this study aimed to compare the muscle activity level of affected leg between Locomat and treadmill walking at a normal speed in people with stroke. This study also investigated the effects of varying BWS and GF on muscle activity patterns during Locomat walking. Therefore, we hypothesized that when walking at a normal speed, people with stroke exhibit lower muscle activity in the affected leg during Locomat walking than during unrestrained treadmill walking. We also hypothesized that reducing BWS and GF will increase muscle activity level of the affected leg in people with stroke.

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Continue —>  Effects of bodyweight support and guidance force on muscle activation during Locomat walking in people with stroke: a cross-sectional study | Journal of NeuroEngineering and Rehabilitation | Full Text

figure1

Placement of electrodes. a: the front view; b: the back view

 

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[Abstract] A Review on Surface Electromyography-Controlled Hand Robotic Devices Used for Rehabilitation and Assistance in Activities of Daily Living

Abstract

Introduction

Spinal cord injuries, traumas, natural aging, and strokes are the main causes of arm impairment or even a chronic disability for an increasing part of the population. Therefore, robotic devices can be essential tools to help individuals afflicted with hand deficit with the activities of daily living in addition to the possibility of restoring hand functions by rehabilitation. Because the surface electromyography (sEMG) control paradigm has recently emerged as an interesting intention control method in devices applied to rehabilitation, the concentration in this study has been devoted to sEMG-controlled hand robotic devices, including gloves and exoskeletons that are used for rehabilitation and for assistance in daily activities.

Materials and Methods

A brief description is given to the previous reviews and studies that have surveyed the robotic devices used for rehabilitation; a comparison is conducted among these studies with respect to the targeted part of the body and the device’s control method. Important issues about controlling by sEMG signal are accentuated, and a review of sEMG-controlled hand robotic devices is presented with an abbreviated description for each endeavor. Some criteria related to sEMG control are specifically emphasized, for instance, the muscles used for control, the number of sEMG channels, and the type of sEMG sensor used.

Discussion

It is noted that most of the sEMG-based controls for the devices included in this study have used the nonpattern recognition scheme due to the weak sEMG signals and abnormal pattern of muscle activation for stroke patients. In addition to sEMG-based control, additional control paradigms have been used in many of the listed robotic devices to increase the efficacy of the system; this cooperation is required because of the difficulty in dealing with the sEMG signals of stroke patients. Most of the listed studies have conducted the experiments on a healthy subject to evaluate the efficacy of the systems, whereas the studies that have recruited stroke patients for system assessment were predominately using additional control schemes.

Conclusions

This article highlights the important issues about the sEMG control method and accentuates the weaknesses associated with this type of control to assist researchers in overcoming problems that impede sEMG-controlled robotic devices to be feasible and practical tools for people afflicted with hand impairment.

via A Review on Surface Electromyography-Controlled Hand Robotic… : JPO: Journal of Prosthetics and Orthotics

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[ARTICLE] The effect of aquatic and treadmill exercise in individuals with chronic stroke – Full Text

ABSTRACT

We compared the effect of gait training on treadmill versus deep water on balance and gait in 12 ischemic stroke chronic survivors randomly sorted to the Pool or Treadmill Groups. Berg Scale (BBS) and timed up and go test (TUG) were applied before and after the interventions. Just one person applied all tests and she was blinded for the aims of the study. Surface EMG of the paretic and non-paretic (NP) side muscles were recorded during walking on a treadmill. Three 100-ms epochs were extracted from the EMG related to gait phases: weight acceptance; propulsion; and pre-strike. For each epoch, we calculated the RMS of the EMG signal. Participants did gait training for 9 weeks (3 times/week, 40 minutes/session). The Pool group did the deep-water walking with a swimming belt. The Treadmill group walked on the treadmill at the maximum speed they could stand. The Manova group compared the effect of training, group, side, muscles, and gait phase into the EMG. Anova was used to test the effect of training, group side, and gait phase into BBS, TUG and EMG variables. Pool and Treadmill had increased balance and agility. The highest EMG RMS occurred at the paretic side, for the Treadmill and after training. The mm. tibialis anterior, gastrocnemius lateralis, vastus lateralis, and biceps femoris presented the highest RMS for the NP side; while for mm. rectus femoris and semitendinosus, the paretic side presented the highest RMS. Thus, the both types of exercise lead to similar functional adaptations with different muscular activations during walking.

INTRODUCTION

Stroke is the second most common cause of death worldwide and the primary cause of chronic disability in adults1), (2. Without intense rehabilitation during the early days after the stroke, neural injuries gradually develop more pronounced motor impairments due to muscle weakness, spasticity and coordination loss3. Later, stroke survivors with chronic impairment become less independent to perform daily life activities, have less social interaction and are more concerned about their future4. Such dependent person with less social life can be considered as having lost motivation. This deprivation occurs because chronic stroke survivors have small resistance to fatigue4)- (6. They do not feel motivated to move continuously or for long periods since they get easily fatigued; as such, rehabilitation programs for such population should spare their activities between motor rehabilitation and increase in physical fitness in order to increase their resistance to fatigue.

In fact, about 80% of stroke survivors can walk without assistance; but their slow walk constrains their daily life activities7), (8. Walking speed is an important outcome for performance evaluation and for functional evaluation in stroke9), (10. The slow walking is due to the lower limb muscles spasticity11)- (14, muscle weakness, postural imbalance and fear of falling. Those clinical impairments also change the gait biomechanics15), (16, inducing asymmetrical, stereotyped and low ranged compensatory movements17. At the early stage of the rehabilitation program, efforts should be addressed to improve body functions in enhance resistance to fatigue.

The aerobic training applied to stroke people enhances physical ability and improves life independence and quality, reducing morbidity and mortality18. Standard aerobic training is usually developed with walking and running. Treadmill protocols to stroke people can recover impaired gait, improve gait parameters and reduce walking asymmetries19. On the other hand, water walking enhances the afferent sensory inflow and improves peak aerobic capacity and walking endurance, being able to affect gait kinematics in patients with stroke18), (20)- (22. It is not clear whether walking on water would provide the same or more benefits compared with the standard treadmill walking for chronic stroke people. In fact, little information is available to support a rehabilitation program for chronic stroke people with reduced mobility. The aim of this study was to compare the effect of aerobic training treadmill versus aerobic training in water for balance and gait in chronic stroke people. We expect that standard treadmill walking training and water walking training will not have similar biomechanical and functional results; therefore, both types of walking training will lead to similar functional results, but the electrical activity of lower limbs will show different behavior after training. Our first hypothesis is that treadmill gait training and deep-water gait training will lead to similar functional adaptations. Our second hypothesis is that treadmill gait training and deep-water gait training will induce different muscle adaptations that will provide different kinds of muscle activation during the walking test. We believe that training will improve participants’ overall fitness, but training specificity will lead to differing muscle activation during the gait test.[…]

Continue —–>  The effect of aquatic and treadmill exercise in individuals with chronic stroke

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[Abstract] A Method for Self-Service Rehabilitation Training of Human Lower Limbs – IEEE Conference Publication

Abstract

Recently, rehabilitation robot technologies have been paid more attention by the researchers in the fields of rehabilitation medicine engineering and robotics. To assist active rehabilitation of patients with unilateral lower extremity injury, we propose a new self-service rehabilitation training method in which the injured lower limbs are controlled by using the contralateral healthy upper ones. First, the movement data of upper and lower limbs of a healthy person in normal walk are acquired by gait measurement experiments. Second, the eigenvectors of upper and lower limb movements in a cycle are extracted in turn. Third, the linear relationship between the movement of upper and lower limbs is identified using the least squares method. Finally, the results of simulation experiments show that the established linear mapping can achieve good accuracy and adaptability, and the self-service rehabilitation training method is effective.

via A Method for Self-Service Rehabilitation Training of Human Lower Limbs – IEEE Conference Publication

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[ARTICLE] A comparison of the rehabilitation effectiveness of neuromuscular electrical stimulation robotic hand training and pure robotic hand training after stroke: A randomized controlled trial – Full Text

Highlights

The rehabilitation effects of the NMES robotic hand and robotic hand were compared.

Both training systems could significantly improve the motor function of upper limb.

The NMES robot was more effective than the pure robot.

NMES applied on distal muscle could benefit the recovery in the entire upper limb.

 

Abstract

Objective

To compare the rehabilitation effects of the electromyography (EMG)-driven neuromuscular electrical stimulation (NMES) robotic hand and EMG-driven robotic hand for chronic stroke.

Methods

This study was a randomized controlled trial with a 3-month follow-up. Thirty chronic stroke patients were randomly assigned to receive 20-session upper limb training with either EMG-driven NMES robotic hand (NMES group, n = 15) or EMG-driven robotic hand (pure group, n = 15). The training effects were evaluated before and after the training, as well as 3 months later, using the clinical scores of Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS), Action Research Arm Test (ARAT), and Functional Independence Measure (FIM). Session-by-session EMG parameters, including the normalized EMG activation level and co-contraction indexes (CIs) of the target muscles were applied to monitor the recovery progress in muscular coordination patterns.

Results

Both groups achieved significantly increased FMA and ARAT scores (p < 0.05), and the NMES group improved more (p < 0.05). A significant improvement in MAS was obtained in the NMES group (p < 0.05) but absence in the pure group. Meanwhile, better performance could be obtained in the NMES group in releasing the EMG activation levels and CIs than the pure group across the training sessions (p < 0.05).

Conclusion

Both training systems were effective in improving the long-term distal motor functions in upper limb, where the NMES robot-assisted training achieved better voluntary motor recovery and muscle coordination and more release in muscle spasticity.

Significance

This study indicated more effective distal rehabilitation using the NMES robot than the pure robot-assisted rehabilitation.

1. Introduction

Upper limb motor deficits are common after stroke, and observed in over 80% of stroke survivors [1,2]. Various rehabilitation devices have been purposed to assist human physical therapists to provide effective long-term rehabilitation programs [[3][4][5]]. Among them, rehabilitation robots and neuromuscular electrical stimulation (NMES) are most widely used in stroke rehabilitation practices. Rehabilitation robots have been recognized as efficient in such cases and could represent a cost-effective addition to conventional rehabilitation services because they provide highly intensive and repetitive training [[6][7][8][9]]. It has been reported that the integration of voluntary effort (e.g. electromyography, EMG) into robotic design could contribute significantly to motor recovery in stroke patients [6,10]. This is because an EMG-driven strategy can maximize the involvement of voluntary effort in the training, and its effectiveness at improving upper limb voluntary motor functions have been proved by many EMG-driven robot-assisted upper-limb training systems [[11][12][13]]. However, rehabilitation robots are unable to directly activate the desired muscle groups, which may only assist, or even dominate limb movement such as continuous passive motions (CPM) [14]. In addition, stroke patients usually cooperate with compensatory motions from other muscular activities to activate the target muscles, which may lead to ‘learned disuse’ [15]. However, NMES can effectively limit compensatory motions by stimulating specific muscles via cyclic electrical currents, which provides repetitive sensorimotor experiences [16]. With the advantage of precisely activating the target muscle, NMES has been reported to be effective in evoking sensory feedback, improving muscle force, and thus promoting motor function in stroke patients [17,18]. Nevertheless, training programs assisted by NMES alone are also suboptimal due to the difficulty of controlling movement trajectories and the early appearance of fatigue [19,20].

Accordingly, various NMES robot-assisted upper-limb training programs which combine these two unique techniques have been proposed to integrate the benefits and minimize the disadvantages [7,12,14,21,22]. The rehabilitation effectiveness of these combined systems has been investigated and reported to be effective in improving motor recovery. Several studies have compared the training outcomes of NMES robot-assisted training and other training programs. For example, Qian et al. [22] reported that NMES-robot-assisted upper-limb training could achieve better motor outcomes when compared with conventional therapies for subacute stroke patients. Meanwhile, another study which compared the training effects between robot-aided training with NMES and robot-aided training solely using the InMotion ARM™ Robot in the subacute period demonstrated that the active ranges of motion of the NMES robot-training group were significantly higher compared with the robot-training group [23]. Coincidentally, investigations into applications in chronic stroke patients have also been carried out. For instance, Hu et al. [14] proposed an EMG-driven NMES robot system for wrist training; this combined device improved muscle activation levels related to the wrist and reduced compensatory muscular activities at the elbow, while these training outcomes were absent for the EMG-driven robot-assisted training alone. Indeed, a similar study by another research group also achieved better rehabilitation outcomes on some clinical assessments using the combined system compared to robot-assisted therapy alone [21].

In the literature, most studies on current rehabilitation devices combining the NMES and robotic systems targeted the elbow and wrist joints [7,[21][22][23]], while very few focused on the hand and fingers [24]. In addition, a comparison of the training effects for hand rehabilitation between the NMES robot and other hand rehabilitation devices has not yet been adequately conducted. Indeed, the primary upper-limb disability post-stroke is the loss of hand function, and rehabilitation of the distal joints after stroke is much more difficult than the motor recovery of the proximal joints due to the compensatory motions from the proximal joints [25]. Hence, developing effective rehabilitation devices to minimize compensatory movements for hand motor recovery is especially meaningful for stroke rehabilitation. In our previous work, we developed an EMG-driven NMES robotic hand and suggested it for use in hand rehabilitation after stroke [26]. Our device provides fine control of hand movements and activates the target muscles selectively for finger extension/flexion, and its feasibility and effectiveness have been verified by a single group trial [12]. However, whether the long-term rehabilitation effect of this EMG-driven NMES robotic hand is comparable or even better than other hand rehabilitation devices are still unclear and need to be investigated quantitively. Therefore, the objective of this study is to compare the training effects of hand rehabilitation assisted by an NMES robotic hand and by a pure robotic hand though a randomized controlled trial with a 3-month follow-up (3MFU).

2. Methodology

2.1. Participants

This work was approved by the Human Subjects Ethics Sub-Committee of the Hong Kong Polytechnic University. A total of 53 stroke survivors were screened for the training from local districts. 30 participants with chronic stroke satisfied the following inclusion criteria: (1) The participants were at least 6 months after the onset of a singular and unilateral brain lesion due to stroke, (2) both the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints could be extended to 180° passively, (3) muscle spasticity during extension at the finger joints and the wrist joint was below 3 as measured by the Modified Ashworth Scale (MAS) [27], ranged from 0 (no increase in muscle tone) to 4 (affected part rigid), (4) detectable voluntary EMG signals from the driving muscle on the affected side (three times of the standard deviation (SD) above the EMG baseline), and (5) no visual deficit and able to understand and follow simple instructions as assessed by the Mini-Mental State Examination (MMSE > 21) [28].

This work involved a randomized controlled trial with a 3-month follow-up (3MFU). The potential participants were first told that the training program they would receive could be either NMES robotic hand training or pure robotic hand training, and all recruited participants submitted their written consent before randomization. Then, the recruited participants were randomly assigned into two groups according to a computer-based random number generator, i.e., the computer program generated either “1” (denoting the NMES robotic hand training group) or “2” (the pure robotic hand group) with an equal probability of 0.5 (Matlab, 2017, Mathworks, Inc.). Fig. 1 shows the Consolidated Standards of Reporting Trials flowchart of the training program.

Fig. 1

Fig. 1. The consolidated standards of reporting trials flowchart of the experimental design.

2.2. Interventions

For both groups, each participant was invited to attend a 20-session robotic hand training with an intensity of 3–5 sessions/week, completed within 7 consecutive weeks. The training setup of both groups is shown in Fig. 2. This robotic hand training system can assist with finger extension and flexion of the paretic limb for patients after stroke. In this work, real-time voluntary EMG detected from the abductor pollicis brevis (APB) and extensor digitorum (ED) muscles were used to control the respective hand closing and opening movements, and the threshold level of each motion phase was set at three times the SD above the EMG baseline at resting state [12]. For example, during the motions of finger flexion, once the EMG activation level of the APB muscle reached a preset threshold, the robotic hand would provide mechanical assistance for hand closing. Similarly, during the motions of finger extension, the robotic hand would assist with hand opening when the EMG activation level of the ED muscle reached a preset threshold. For the NMES robot group, synchronized support from the NMES and the robot were both provided. The NMES electrode pair (30 mm diameter; Axelgaard Corp., Fallbrook, CA, USA) was attached over the ED muscle to provide stimulation during finger extension. The outputs of NMES were square pulses with a constant amplitude of 70 V, a stimulation frequency of 40 Hz, and a manually adjustable pulse width in the range 0–300 μs. Before the training, the pulse width was set at the minimum intensity, which achieved a fully extended position of the fingers in each patient. During the training, NMES would be triggered by the EMG from the ED muscle first and then provided stimulation to the ED muscle to assist hand-opening motions for the entire phase of finger extension, while no assistance from NMES was provided during finger flexion to avoid the possible increase of finger spasticity after stimulation [29]. For the pure robot group, the difference between the two groups was that no NMES was applied in the pure robot group. A detailed account of the working principles of the robotic hand have been described in our previous work [12,30,31].

Fig. 2

Fig. 2. The experimental setup of the robotic hand training: (A) pure robotic hand group; (B) neuromuscular electrical stimulation (NMES) robotic hand group.

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Continue —-> A comparison of the rehabilitation effectiveness of neuromuscular electrical stimulation robotic hand training and pure robotic hand training after stroke: A randomized controlled trial – ScienceDirect

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[ARTICLE] Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback – Full Text

Abstract

Background

Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such robot-mediated interventions routinely use also visual biofeedback tools. A better understanding of biofeedback content effects when used for robotic locomotor training may improve the rehabilitation process and outcomes.

Methods

This randomized cross-over pilot trial aimed to address the possible impact of different biofeedback contents on patients’ performance and experience during Lokomat training, by comparing a novel biofeedback based on online biological electromyographic information (EMGb) versus the commercial joint torque biofeedback (Rb) in sub-acute non ambulatory patients.

12 patients were randomized into two treatment groups, A and B, based on two different biofeedback training. For both groups, study protocol consisted of 12 Lokomat sessions, 6 for each biofeedback condition, 40 min each, 3 sessions per week of frequency. All patients performed Lokomat trainings as an add-on therapy to the conventional one that was the same for both groups and consisted of 40 min per day, 5 days per week. The primary outcome was the Modified Ashworth Spasticity Scale, and secondary outcomes included clinical, neurological, mechanical, and personal experience variables collected before and after each biofeedback training.

Results

Lokomat training significantly improved gait/daily living activity independence and trunk control, nevertheless, different effects due to biofeedback content were remarked. EMGb was more effective to reduce spasticity and improve muscle force at the ankle, knee and hip joints. Robot data suggest that Rb induces more adaptation to robotic movements than EMGb. Furthermore, Rb was perceived less demanding than EMGb, even though patient motivation was higher for EMGb. Robot was perceived to be effective, easy to use, reliable and safe: acceptability was rated as very high by all patients.

Conclusions

Specific effects can be related to biofeedback content: when muscular-based information is used, a more direct effect on lower limb spasticity and muscle activity is evidenced. In a similar manner, when biofeedback treatment is based on joint torque data, a higher patient compliance effect in terms of force exerted is achieved. Subjects who underwent EMGb seemed to be more motivated than those treated with Rb.

Background

Stroke is the leading cause of acquired disability throughout the world, with increasing survival rates as medical care and treatment techniques improve [1]. Post-stroke disability often affects mobility, balance, and walking [2]. The majority of stroke survivors rank walking recovery among their top rehabilitation goals [3,4,5]. Furthermore, the ability to walk is one of the most important determining factors for returning home after stroke [4].

Recovery of walking mainly occurs within the first 11 weeks after a stroke [6]; indeed, further recovery after that time is rare [7]. Overall, between 30 and 40% of stroke survivors are not able to regain a functional gait after rehabilitation [48]. These data have stimulated advances in many different innovative technological approaches to improve the gait rehabilitation efficacy.

Modern concepts favour task-specific repetitive rehabilitation approaches [9], with high intensity [10] and early multisensory stimulation [11]. These requirements are met by robot assisted gait training (RAGT) approaches. Recent studies on stroke patients have reported that when conventional therapy and RAGT are combined, compared to conventional therapy alone, gait recovery significantly improves [12] and patients are more likely to recover independent walking [13]. In particular, non-ambulatory patients in the sub-acute phase are the group most likely to benefit from this type of training [13].

This high interest in robotic therapy has attracted attention to human robot interactions in the rehabilitation framework, and a consensus is forming on the importance of top-down approaches in rehabilitation, particularly when dealing with robotic devices [14]. The critical aspects of top-down approaches are multifarious and include motivation, active participation [15], learning skills [16] and error-driven-learning [17], evidencing the key aspects of biofeedback information to guide and improve patient robot interactions.

Thus, biofeedback is, at present, the main approach to guide top-down control mechanisms, which represents a powerful tool to drive recovery. To this aim, the patient has to be aware of the differences between on-line performance and the desired performance [18]. In this scenario, many different error signals can be used, and at present, no indication exists for their specific effects on performances [1819]. Many biological parameters have been used to feed biofeedback information to patients in different stroke gait rehabilitation scenarios [20].

In general, in spite of the information content, biofeedback has been associated with improved outcomes in several gait pathologies [21,22,23,24]. Among diverse types of biofeedback, the most generally employed in gait rehabilitation paradigms have been electromyographic (EMG), kinematic as well as robot generated indexes [25], although no comparisons have been made among these approaches.

At present, many robotic devices for gait rehabilitation in stroke are commercially available [26]. Two main classes can be identified, those based on body weight support systems (BWSS) and over ground exoskeletons. Overall, BWSS are the most widely used in rehabilitation centres, with Lokomat, Gait Trainer and GEO systems being the most popular. The present study focuses on the biofeedback content effects during Lokomat gait training in stroke survivors. Commercially available Lokomat biofeedback tools are based either on navigational or robot-generated information. The latter approach focuses on the forces that assist patients to follow the predefined gait pattern due to force transducers built into the robot drives [25].

Generally effectiveness of Lokomat training is assessed with gait functional outcome measures. Specific data about spasticity effects of Lokomat training are rare, and mainly focused on spinal cord injury (SCI) patients and on ankle muscles. In this framework few studies addressed positive effects of Lokomat training on reducing spasticity and improving volitional control of the spastic ankle in persons with incomplete SCI [27], and on reducing the abnormal modulation of neuromuscular properties that arises as secondary effects after SCI [2829]. To our knowledge, as concern stroke population, a single study compared conventional rehabilitation versus Lokomat add-on training selecting spasticity as a secondary outcome, demonstrating no significant robotic gait training effects [30].

Furthermore, no studies have either analysed the use of an electromyographic -based biofeedback (EMGb) of hip, knee and ankle muscles during training with the Lokomat robot, or compared the impact of different biofeedback types on Lokomat robotic gait training. To this end, we designated a randomized controlled trial, because this type of study is the most rigorous and robust research method of determining whether a cause-effect relation exists between an intervention and an outcome [31]. In this pilot study we compared two different types of biofeedback: a robot generated joint torque biofeedback (Rb) versus a novel on-line EMGb. Thus, a randomized cross-over clinical trial using the Lokomat RAGT device, was conducted focusing on patients’ performances, personal experience and robot forces data in sub-acute non ambulatory patients. In particular the main outcome measure was considered the lower limb spasticity. Considering that in stroke population, spasticity may affect quality-of-life and can be highly detrimental to daily function [32], we also analysed patients’ personal experience related to training gait with the Lokomat system.[…]

 

Continue —> Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback | Journal of NeuroEngineering and Rehabilitation | Full Text

figure3

Representative image of visual biofeedback provided to the patient (PT6) according to on-line EMG activity during first (a) and last (b) EMGb training session. EMG data were displayed on the screen with 4 colour stripes partitioned into 16 stages within the gait cycle. First stripe referred to VL-RF, second stripe refers to BF, third stripe referred to GM-SOL and last stripe referred to TA. Coloured lines in the patient’s feedback were generated as follows: i) Red colour means that the signal is higher than in the template, or ii) Blue means that the signal is lower than in the template. From Fig. 3-b is evident a more physiological muscle activity during the whole gait cycle

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[ARTICLE] Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling – Full Text

Abstract

Background

Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery.

Methods

We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time.

Results

We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients.

Conclusions

Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.

Background

The ability to walk directly relates to quality of life. Neurological lesions such as those underlying stroke and spinal cord injury (SCI) often result in severe motor impairments (i.e., paresis, spasticity, abnormal joint couplings) that compromise an individual’s motor capacity and health throughout the life span. For several decades, scientific effort in rehabilitation robotics has been directed towards exoskeletons that can help enhance motor capacity in neurologically impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had limited performance when tested in healthy individuals [1] and have achieved only modest clinical impact in neurologically impaired patients [2], e.g., stroke [34], SCI patients [5]. […]

 

Continue —>  Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1

Fig. 1 Enter aSchematic representation of the real-time modeling framework and its communication with the robotic exoskeleton. The whole framework is operated by a Raspberry Pi 3 single-board computer. The framework consists of five main components: a The EMG plugin collects muscle bioelectric signals from wearable active electrodes and transfers them to the EMG-driven model. b The B-spline component computes musculotendon length (Lmt) and moment arm (MA) values from joint angles collected via robotic exoskeleton sensors. c The EMG-driven model uses input EMG, Lmt and MA data to compute the resulting mechanical forces in 12 lower-extremity musculotendon units (Table 1) and joint moment about the degrees of freedom of knee flexion-extension and ankle plantar-dorsiflexion. d The offline calibration procedure identifies internal parameters of the model that vary non-linearly across individuals. These include optimal fiber length and tendon slack length, muscle maximal isometric force, and excitation-to-activation shape factors. eThe exoskeleton plugin converts EMG-driven model-based joint moment estimates into exoskeleton control commands. Please refer to the Methods section for an in-depth description caption

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[Abstract + References] Electromyography as a Suitable Input for Virtual Reality-Based Biofeedback in Stroke Rehabilitation – Conference paper

Abstract

Virtual reality (VR)-based biofeedback of brain signals using electroencephalography (EEG) has been utilized to encourage the recovery of brain-to-muscle pathways following a stroke. Such models incorporate principles of action observation with neurofeedback of motor-related brain activity to increase sensorimotor activity on the lesioned hemisphere. However, for individuals with existing muscle activity in the hemiparetic arm, we hypothesize that providing biofeedback of muscle signals, to strengthen already established brain-to-muscle pathways, may be more effective. In this project, we aimed to understand whether and when feedback of muscle activity (measured using surface electromyography (EMG)) might more effective compared to EEG biofeedback. To do so, we used a virtual reality (VR) training paradigm we developed for stroke rehabilitation (REINVENT), which provides EEG biofeedback of ipsilesional sensorimotor brain activity and simultaneously records EMG signals. We acquired 640 trials over eight 1.5-h sessions in four stroke participants with varying levels of motor impairment. For each trial, participants attempted to move their affected arm. Successful trials, defined as when their EEG sensorimotor desynchronization (8–24 Hz) during a time-limited movement attempt exceeded their baseline activity, drove a virtual arm towards a target. Here, EMG signals were analyzed offline to see (1) whether EMG amplitude could be significantly differentiated between active trials compared to baseline, and (2) whether using EMG would have led to more successful VR biofeedback control than EEG. Our current results show a significant increase in EMG amplitude across all four participants for active versus baseline trials, suggesting that EMG biofeedback is feasible for stroke participants across a range of impairments. However, we observed significantly better performance with EMG than EEG for only the three individuals with higher motor abilities, suggesting that EMG biofeedback may be best suited for those with better motor abilities.

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