Early Stroke Rehabilitation of the Upper Limb Assisted with an Electromyography-Driven Neuromuscular Electrical Stimulation-Robotic Arm

Great, In 50 years this might make it to general practice, but until then you are screwed because your doctor never reads and implements research. Ask her/him when was the last time they themselves brought something new into the stroke practice.  I wonder if this would stop spasticity from developing in the arm? We'll never know.

Early Stroke Rehabilitation of the Upper Limb Assisted with an Electromyography-Driven Neuromuscular Electrical Stimulation-Robotic Arm

Qiuyang Qian¹, Xiaoling Hu¹*, Qian Lai¹, Stephanie C. Ng², Yongping Zheng¹ and Waisang Poon²

• ¹Interdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
• ²Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, Hong Kong

Background: Effective poststroke motor rehabilitation depends on repeated limb practice with voluntary efforts. An electromyography (EMG)-driven neuromuscular electrical stimulation (NMES)-robot arm was designed for the multi-joint physical training on the elbow, the wrist, and the fingers.

Objectives: To investigate the training effects of the device-assisted approach on subacute stroke patients and to compare the effects with those achieved by the traditional physical treatments.

Method: This study was a pilot randomized controlled trial with a 3-month follow-up. Subacute stroke participants were randomly assigned into two groups, and then received 20-session upper limb training with the EMG-driven NMES-robotic arm (NMES-robot group, n = 14) or the time-matched traditional therapy (the control, n = 10). For the evaluation of the training effects, clinical assessments including Fugl-Meyer Assessment (FMA), Modified Ashworth Score (MAS), Action Research Arm Test (ARAT), and Function Independence Measurement (FIM) were conducted before, after the rehabilitation training, and 3 months later. Session-by-session EMG parameters in the NMES-robot group, including normalized co-contraction Indexes (CI) and EMG activation level of target muscles, were used to monitor the progress in muscular coordination patterns.

Results: Significant improvements were obtained in FMA (full score and shoulder/elbow), ARAT, and FIM [P < 0.001, effect sizes (EFs) > 0.279] for both groups. Significant improvement in FMA wrist/hand was only observed in the NMES-robot group (P < 0.001, EFs = 0.435) after the treatments. Significant reduction in MAS wrist was observed in the NMES-robot group after the training (P < 0.05, EFs = 0.145) and the effects were maintained for 3 months. MAS scores in the control group were elevated following training (P < 0.05, EFs > 0.24), and remained at an elevated level when assessed 3 months later. The EMG parameters indicated a release of muscle co-contraction in the muscle pairs of biceps brachii and flexor carpi radialis and biceps brachii and triceps brachii, as well as a reduction of muscle activation level in the wrist flexor in the NMES-robot group.

Conclusion: The NMES-robot-assisted training was effective for early stroke upper limb rehabilitation and promoted independence in the daily living comparable to the traditional physical therapy. It could achieve higher motor outcomes at the distal joints and more effective release in muscle tones than the traditional therapy.

Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02117089; date of registration: April 10, 2014.

Introduction

Stroke is one of the leading causes of permanent disability in adults (1). Approximately 80% stroke survivors regain their walking independence (2). However, less than 25% survivors could achieve some limited recovery on the upper limb function, and only around 5% of them could obtain complete functional recovery 6 months later after the onset (2, 3). Dysfunctions in the upper limb are a combination of muscle weakness, spasticity, and discoordination among different muscle groups (4, 5). Significant spontaneous motor recovery usually occurs within the first several weeks to 6 months after stroke, i.e., in the subacute period (6). Physical rehabilitation in this early period can optimize the spontaneous neural plasticity and motor responsiveness, and result in maximized motor outcomes (7, 8). In comparison with the rehabilitation treatment administrated in the chronic period (i.e., 6 months later after the onset), motor functions resorted in the subacute period are more likely to be generalized into functional activities in the daily life (9, 10). One of the major reasons is that the persons with subacute stroke have not been used to adopt the unaffected limb only for daily tasks as commonly observed in the chronic. The traditional rehabilitation treatments in early stage after stroke are usually conducted manually by human therapists, which are time consuming and labor demanding (5). It is challenging to the current medical and health-care system to provide adequate or intensive rehabilitation treatments to persons with subacute stroke, due to the lack of professional manpower in the physical therapy industry even in developed countries (11) and the expanding of stroke populations worldwide (3).

Effective motor restoration after stroke depends on repeated and intensive practice of the paralyzed limbs with voluntary efforts (7, 12, 13). Repetitive practice with high-intensity has been proven to speed up the process of motor restorations (6, 13). The involvement of voluntary effort from the residual neuromuscular pathways has been convinced to carry out better performance with higher efficiency when compared with the continuous passive motion trainings (14, 15). Coordinated upper limb practices among different joints, especially the involvement of the distal joints (e.g., the wrist and fingers) have also been found more effective to translate the motor improvements into meaningful limb functions than single joint practice (16). However, due to the overall muscle weakness in early stage after stroke and a delayed motor return at the distal joints in comparison with the proximal, it is always a difficulty for human therapists to instruct and support the coordinated upper limb motions with the proximal (i.e., the shoulder and the elbow) and distal joints (i.e., the wrist and the fingers) together in the clinical practice (17). New techniques are needed to assist in the manually conducted upper limb coordinating rehabilitation.

Rehabilitation robots can assist human therapists to conduct the intensive and repeated physical training with different numbers and sizes of electrical motors. Various robots have been designed for poststroke upper limb rehabilitation (18–21). Among them, the robots with the involvement of voluntary efforts from the residual neuromuscular pathways demonstrated better rehabilitation effects than those with passive limb motions, i.e., the limb motions are entirely dominated by the machine (18). It has been found that physical trainings with passive motions only contributed to the temporary release of muscle spasticity. However, voluntary practice could improve the motor functions of the limb with longer sustainability (18, 22). Neuromuscular electrical stimulation (NMES) is a technique that can generate limb movements by applying electrical current on the paretic muscles (23). Poststroke rehabilitation assisted with NMES has been found to effectively prevent muscle atrophy and improve muscle strength (23, 24), and the stimulation also evokes sensory feedback to the brain during muscle contraction to facilitate motor relearning (25). NMES can improve limb functions by limiting “learned disuse” that stroke survivors are gradually accustomed to managing their daily activities without using certain muscles, which has been considered as a significant barrier to maximize the recovery (26). However, NMES alone is hard to achieve desired accuracy in kinematics, such as speed and trajectories, as in the robot-assisted training (27).

In our previous works, we designed a series of voluntary intention-driven rehabilitation robotics for physical training at the elbow, the wrist, and fingers (22, 28–31). Residual electromyography (EMG) from the paretic muscles was used to control the robots to provide assistive torques to the limb for desired motions (31, 32). Later, we integrated NMES into the EMG-driven robot as an intact system for wrist rehabilitation. It has been found that the combined assistance with both robot and NMES could reduce the excessive muscular activities at the elbow and improve the muscle activation levels related to the wrist in chronic stroke, which was absent in the pure robot-assisted training (31). Pure robot-assisted upper limb training also showed no superiority on motor improvements on chronic stroke in comparison with the traditional treatments in a reported randomized controlled trial (33). More recently, combined treatment with robot and NMES for the wrist by other research group also demonstrated more promising rehabilitation effectiveness in the upper limb motor recovery than pure robot training (34). However, most of the proposed devices are for single joint treatment, and the related trials were conducted on chronic stroke. We hypothesized that poststroke multi-joint coordinated training with both NMES and robot in the subacute stroke period could improve the muscular coordination in the whole upper limb and translate the motor improvements into daily functions. In this work, we developed an EMG-driven NMES-robotic arm for multi-joint coordinated training on the elbow, wrist, and fingers. The feasibility of the EMG-driven NMES-robotic arm assisted upper limb training on subacute stroke, and the training effectiveness were investigated through a pilot randomized controlled trial in comparison with the traditional upper limb physical rehabilitation.

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EMG-Based Continuous and Simultaneous Estimation of Arm Kinematics in Able-Bodied Individuals and Stroke Survivors

I have no understanding of anything said here. So go ask your doctor how they will use this to get you recovered. 

EMG-Based Continuous and Simultaneous Estimation of Arm Kinematics in Able-Bodied Individuals and Stroke Survivors

Jie Liu¹, Sang Hoon Kang², Dali Xu¹, Yupeng Ren¹, Song Joo Lee³ and Li-Qun Zhang^4,5*

• ¹Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, United States
• ²School of Mechanical, Aerospace, and Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
• ³Center for Bionics, Biomedical Research Institute, Korea Institute of Science and Technology, University of Science and Technology, Seoul, South Korea
• ⁴Department of Physical Therapy and Rehabilitation Science and Department of Orthopaedics, University of Maryland, Baltimore, MD, United States
• ⁵Department of Bioengineering, University of Maryland, College Park, MD, United States

Among the potential biological signals for human-machine interactions (brain, nerve, and muscle signals), electromyography (EMG) widely used in clinical setting can be obtained non-invasively as motor commands to control movements. The aim of this study was to develop a model for continuous and simultaneous decoding of multi-joint dynamic arm movements based on multi-channel surface EMG signals crossing the joints, leading to application of myoelectrically controlled exoskeleton robots for upper-limb rehabilitation. Twenty subjects were recruited for this study including 10 stroke subjects and 10 able-bodied subjects. The subjects performed free arm reaching movements in the horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface EMG signals from six muscles crossing the three joints were recorded. A non-linear autoregressive exogenous (NARX) model was developed to continuously decode the shoulder, elbow and wrist movements based solely on the EMG signals. The shoulder, elbow and wrist movements were decoded accurately based only on the EMG inputs in all the subjects, with the variance accounted for (VAF) > 98% for all three joints. The proposed approach is capable of simultaneously and continuously decoding multi-joint movements of the human arm by taking into account the non-linear mappings between the muscle EMGs and joint movements, which may provide less effortful control of robotic exoskeletons for rehabilitation training of individuals with neurological disorders and arm impairment.

Introduction

Rehabilitation robots have been developing rapidly and used for therapeutic training of patients with neurological disorders, including stroke, cerebral palsy, and spinal cord injury (Dipietro et al., 2005; Krebs et al., 2008; Song et al., 2008; Marchal-Crespo and Reinkensmeyer, 2009; Pons, 2010; Frisoli et al., 2012; Heo et al., 2012; Reinkensmeyer and Boninger, 2012; Zariffa et al., 2012; Ren et al., 2013). Advances have been made to build more practical and functional upper-limb powered robotic exoskeleton devices (Nef et al., 2007; Perry et al., 2007; Gupta et al., 2008; Kim et al., 2012; Mao and Agrawal, 2012; Ren et al., 2013; Shao et al., 2014). The advances in powered exoskeletons imply great promise to allow neurologically impaired patients to perform versatile activities, therefore helping restore strength and flexibility of their limbs. In contrast, relatively less attention has been given to providing less effortful control of exoskeleton robots. Previous studies have shown that motor commands are generated by the combination of a small number of muscle synergies, which allows the coordinated recruitment of groups of muscles with specific amplitude balances (Jiang et al., 2010; Overduin et al., 2015; d'Avella, 2016). Muscle synergies can be used to predict the movement of multiple degrees of freedom (DOFs; Ison and Artemiadis, 2014; Jiang et al., 2014a). Controlling a multiple DOFs robotic device requires sophisticated techniques for identification of various movements from recorded electromyography (EMG) signals (Fleischer and Hommel, 2008; Lo et al., 2010; Peerdeman et al., 2011; Scheme and Englehart, 2011; Fougner et al., 2012). A neural control interface is crucial to providing accurate, natural and less effortful control of powered exoskeletons (Kiguchi and Hayashi, 2012; Lenzi et al., 2012).

Among the potential biological signals for human-machine interaction (brain, nerve, and muscle signals), EMG, the ensemble electrical activity of a muscle may be the only experimentally non-invasive record of the motor commands to the muscles that enables routine clinical applications. EMG is generated by the neural activation from the brain and spinal cord and therefore contains substantial movement-related information. It is worth noting that EMG signals do not necessarily reflect the overall computations carried on by the motor system. In fact, they are unlikely to catch neural signals related to a key executive function for shaping motor behavior, i.e., the ability of cancelling pending movements (Mirabella, 2014; Mirabella and Lebedev, 2017). However, in practice EMG signals could enable efficient control of robotic exoskeletons by extracting those motor commands that reach the muscles. Inhibitory control is fundamental for achieving a proper behavioral flexibility due to the fact events cannot be fully predicted practically. In many instances, preplanned actions must be aborted to avoid catastrophic consequences. Often suppression of a planned action occurs within the central nervous system, and thus the related neural activity does not reach the muscles. It is not by chance that brain–machine interfaces enacting inhibitory control have proposed to reproduce goal-directed behaviors in a more naturalistic way recently (Mirabella, 2012; Mirabella and Lebedev, 2017).

Use of EMG in decoding motor commands is one of the most robust and accurate interfaces for controlling robotic devices (Farina and Aszmann, 2014). As a non-invasive measurement containing rich motor control information, surface EMG is an important input for the control of powered robotic devices (Parker et al., 2006; Pons, 2010). As a result, surface EMG is increasingly recognized as one of the important control signals for assistive or rehabilitative devices in robot-aided therapy (Song et al., 2008; Hincapie and Kirsch, 2009; Marchal-Crespo and Reinkensmeyer, 2009; Jiang et al., 2010; Smith and Brown, 2011). Myoelectric control is a promising approach for controlling the multiple DOFs of multifunctional dexterous exoskeletons (Fleischer and Hommel, 2008). However, a major challenge in myoelectric control is to provide simultaneous and proportional control signals for robotic devices with multiple DOFs (Jiang et al., 2010; Fougner et al., 2012). To facilitate a less effortful myoelectric control paradigm, myoelectric controllers should provide proportional control of multiple DOFs simultaneously. This has been addressed in a few recent studies (Ameri et al., 2014a; Farmer et al., 2014; Fougner et al., 2014; Hahne et al., 2014; Ngeo et al., 2014). To provide simultaneous, independent and proportional control of multiple DOFs, various linear and non-linear estimators have been used, including artificial neural networks (Koike and Kawato, 1995; Cheron et al., 1996; Au and Kirsch, 2000; Shrirao et al., 2009; Pulliam et al., 2011; Jiang et al., 2012; Zhang et al., 2012; Ameri et al., 2014b; Farmer et al., 2014; Ngeo et al., 2014), regression techniques (Chen et al., 2013; Ameri et al., 2014a; Hahne et al., 2014), and state-space models (Artemiadis and Kyriakopoulos, 2010, 2011; Pan et al., 2014). Recent research has shown that continuous decoding plays increasingly important role in myoelectric control (Ameri et al., 2014a; Farmer et al., 2014; Fougner et al., 2014; Hahne et al., 2014; Ngeo et al., 2014).

Previous studies have shown that artificial neural networks, being a widely used supervised non-linear approach, outperformed linear regression (a supervised linear approach) and non-negative matrix factorization (a linear unsupervised method) in the EMG decoding paradigm (Hahne et al., 2014; Jiang et al., 2014b). It was possible to predict wrist joint angle instead of forces from EMGs with artificial neural networks when the subject was performing free dynamic movements (Jiang et al., 2012). In particular, a non-linear autoregressive exogenous (NARX) model was utilized to continuously map the kinematics of a transtibial prosthesis and EMG activity to estimate the prosthetic ankle angle in transtibial amputees in a recent study (Farmer et al., 2014). However, few investigators have been able to draw on any systematic research into decoding dynamic multi-joint arm movements. Most studies on EMG decoding have only focused on the estimation of kinematics of the leg and fingers. The purpose of this study was to develop a novel multi-input multi-output decoding method based on the NARX neural network model and predict dynamic multi-joint arm movements simultaneously based only on multi-channel EMG inputs, which can potentially be used to achieve user-friendly, less effortful myoelectric control of robot-aided multi-joint movements.

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Ask your doctor to explain this equation;

To continuously decode the multi-joint positions simultaneously from the EMG LE signals, the following NARX network model was used:

Un(t)=f1(∑Nxi=1∑nxk=0Ani(k)Xi(t−m−k)                   −∑Nyj =1∑nyk=0Bnj(k)Yj(t−m−k)+b1n),                   n=1,2,3    (3)

Yj(t)=f2(∑3n=1Cjn(t)Un(t)+b2j),     j=1,2,3    (4)

Upper Extremity Motor Impairments and Microstructural Changes in Bulbospinal Pathways in Chronic Hemiparetic Stroke

I would postulate that spasticity would be a more logical explanation for these problems.
http://journal.frontiersin.org/article/10.3389/fneur.2017.00257/full?

Meriel Owen^1,2, Carson Ingo¹ and Julius P. A. Dewald^1,2,3*

• ¹Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
• ²Interdepartmental Neuroscience Program, Northwestern University, Chicago, IL, United States
• ³Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States

Following hemiparetic stroke, precise, individuated control of single joints is often replaced by highly stereotyped patterns of multi-joint movement, or abnormal limb synergies, which can negatively impact functional use of the paretic arm. One hypothesis for the expression of these synergies is an increased dependence on bulbospinal pathways such as the rubrospinal (RubST) tract and especially the reticulospinal (RetST) tracts, which co-activate multiple muscles of the shoulder, elbow, wrist, and fingers. Despite indirect evidence supporting this hypothesis in humans poststroke, it still remains unclear whether it is correct. Therefore, we used high-resolution diffusion tensor imaging (DTI) to quantify white matter microstructure in relation to severity of arm synergy and hand-related motor impairments. DTI was performed on 19 moderately to severely impaired chronic stroke individuals and 15 healthy, age-matched controls. In stroke individuals, compared to controls, there was significantly decreased fractional anisotropy (FA) and significantly increased axial and radial diffusivity in bilateral corona radiata and body of the corpus callosum. Furthermore, poststroke, the contralesional (CL) RetST FA correlated significantly with both upper extremity (UE) synergy severity (r = −0.606, p = 0.003) and hand impairment (r = −0.609, p = 0.003). FA in the ipsilesional RubST significantly correlated with hand impairment severity (r = −0.590, p = 0.004). For the first time, we separately evaluate RetST and RubST microstructure in chronic stroke individuals with UE motor impairment. We demonstrate that individuals with the greatest UE synergy severity and hand impairments poststroke have the highest FA in the CL RetST a pattern consistent with increased myelination and suggestive of neuroplastic reorganization. Since the RetST pathway microstructure, in particular, is sensitive to abnormal joint coupling and hand-related motor impairment in chronic stroke, it could help test the effects of specific, and novel, anti-synergy neurorehabilitation interventions for recovery from hemiparesis.

Introduction

Approximately 85% of stroke survivors experience significant motor impairment in the contralesional (CL) arm (1), which can include a loss of independent joint control (2, 3), weakness (4), and spasticity (5). After stroke, precise, individuated control of single joints is often replaced by highly stereotyped patterns of multi-joint movement caused by abnormal muscle co-activation patterns (6). The most prevalent of these patterns is the flexion synergy, which is characterized by an abnormal coupling of shoulder abduction and elbow, wrist, and finger flexion (7, 8). This impairment has a negative impact on reaching ability (9) and hand function (3, 10), both critical components of functional use of the arm during activities of daily living. Despite the debilitating nature of this motor impairment, the underlying neuropathophysiology is not fully understood.

One hypothesis for why the flexion synergy emerges is that following a reduction of corticofugal input from the lesioned hemisphere, there is an increased dependence on CL motor cortex and bulbospinal pathways, such as reticulospinal (RetST) and rubrospinal (RubST) tracts. Therefore, in the present study, we quantify microstructural properties in white matter of both the brain and the brainstem, focusing primarily on corticoreticulospinal and corticorubrospinal systems. We evaluate whether these microstructural properties increase in integrity in relation to arm synergy and hand impairment severity, which could be indicative of increased use.

Although the RetST was previously believed to be predominantly involved in gross movements, such as locomotion (11, 12) and posture (13, 14), recent work in primates suggests the RetST also influences the motor neurons that control forearm and intrinsic hand muscles (15). In the non-human primate, stimulation of the RetST produces ipsilateral wrist flexor, elbow flexor, and shoulder abductor activation (16), mirroring the flexion synergy pattern observed in humans poststroke. Furthermore, stimulating the RetST after a corticospinal tract (CST) lesion elicits increased excitatory post-synaptic potentials in motoneurons innervating the forearm flexor and intrinsic hand muscles (17). This evidence makes the contralesional corticoreticulospinal system a compelling candidate for underlying abnormal joint coupling in humans with hemiparetic stroke.

In the non-human primate, the RubST also contributes to reaching and grasping movements (18) and has been shown to be important in recovery of hand function after CST damage (19, 20). One study showed that increased white matter integrity in bilateral red nucleus (RN) correlated with worse clinical outcomes in humans with chronic stroke (21); however, the RubST has been reported as relatively insignificant in humans (22, 23). The evidence for whether the RetST and the RubST contribute to abnormal joint coupling and hand impairment in humans poststroke still remains indirect and inconclusive.

We used high-resolution diffusion tensor imaging (DTI) (24) tract-based spatial statistics (TBSS) (25) to perform a voxel-wise comparison of white matter microstructure between stroke and control individuals. We analyzed fractional anisotropy (FA), a measurement typically associated with tract integrity, as well as axial diffusivity (AD) and radial diffusivity (RD), which represent diffusion parallel and perpendicular to the principle direction of diffusion, respectively. Because previous studies have reported altered diffusion properties in lesioned tissue (26–28), we excluded potential lesion-compromised voxels from our TBSS analysis to assess changes in normal-appearing white matter. We used the TBSS-derived white matter skeleton to investigate whether microstructural tissue properties within specific regions of the brainstem (CST, RetST, RubST) and subcortical white matter within CL motor areas [primary motor area (M1), premotor area (PM), supplementary motor area (SMA), body of the corpus callosum] are sensitive to upper extremity (UE) motor impairment in chronic stroke individuals.

We evaluated UE motor impairment using the Fugl-Meyer Assessment (FMA), a stroke-specific, performance-based motor impairment index, which measures impairments, such as loss of independent joint function, stretch reflex hyper-excitability, and altered sensation (29). It is one of the most widely used clinical scales of motor impairment poststroke (30). While previous studies have looked at diffusion MRI metrics in relation to the entire FMA score (31, 32), we used only the UE measurements of arm synergies and hand function to determine whether microstructural properties in specific white matter regions of interest (ROIs) were correlated.

In the present study, we hypothesized that microstructural integrity in specific regions of the extrapyramidal brainstem would be increased in chronic stroke in a manner sensitive to synergy and hand-related impairment severity. We demonstrate a significant decrease in FA in bilateral corona radiata and body of the corpus callosum in chronic stroke when compared to controls; however, within stroke subjects, specific brainstem regions show the highest FA in individuals with the most synergy-driven arm and hand impairment. More precisely, we describe the relation between CL RetST integrity and both expression of synergy and hand impairment and between ipsilesional (IL) RubST integrity and hand impairment in chronic hemiparetic stroke individuals.

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