Posts Tagged Electromyography

[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.

 […]

 

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.

References

  1. 1.
    Langhorne, P., Bernhardt, J., Kwakkel, G.: Stroke rehabilitation. Lancet 377, 1693–1702 (2011).  https://doi.org/10.1016/S0140-6736(11)60325-5CrossRefGoogle Scholar
  2. 2.
    Ramos-Murguialday, A., et al.: Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. (2013).  https://doi.org/10.1002/ana.23879CrossRefGoogle Scholar
  3. 3.
    Shindo, K.: Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. J. Rehabil. Med. 43, 951–957 (2016).  https://doi.org/10.2340/16501977-0859CrossRefGoogle Scholar
  4. 4.
    Armagan, O., Tascioglu, F., Oner, C.: Electromyographic biofeedback in the treatment of the hemiplegic hand: a placebo-controlled study. Am. J. Phys. Med. Rehabil. 82, 856–861 (2003).  https://doi.org/10.1097/01.PHM.0000091984.72486.E0CrossRefGoogle Scholar
  5. 5.
    Garrison, K.A., Aziz-Zadeh, L., Wong, S.W., Liew, S.L., Winstein, C.J.: Modulating the motor system by action observation after stroke. Stroke 44, 2247–2253 (2013).  https://doi.org/10.1161/STROKEAHA.113.001105CrossRefGoogle Scholar
  6. 6.
    Celnik, P., Webster, B., Glasser, D.M., Cohen, L.G.: Effects of action observation on physical training after stroke. Stroke. 39, 1814–1820 (2008).  https://doi.org/10.1161/STROKEAHA.107.508184CrossRefGoogle Scholar
  7. 7.
    Vourvopoulos, A., Bermúdezi Badia, S.: Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J. Neuroeng. Rehabil. 13, 1–14 (2016).  https://doi.org/10.1186/s12984-016-0173-2CrossRefGoogle Scholar
  8. 8.
    Spicer, R., Anglin, J., Krum, D.M., Liew, S.L.: REINVENT: a low-cost, virtual reality brain-computer interface for severe stroke upper limb motor recovery. In: Proceedings of IEEE Virtual Reality, pp. 385–386 (2017).  https://doi.org/10.1109/vr.2017.7892338
  9. 9.
    Klem, G.H., Lüders, H.O., Jasper, H.H., Elger, C.: The ten-twenty electrode system of the International Federation. Electroencephalogr. Clin. Neurophysiol. 10, 371–375 (1999).  https://doi.org/10.1016/0013-4694(58)90053-1CrossRefGoogle Scholar
  10. 10.
    Kothe, C.: Lab Streaming Layer (LSL). https://github.com/sccn/labstreaminglayer
  11. 11.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9–21 (2004).  https://doi.org/10.1016/j.jneumeth.2003.10.009CrossRefGoogle Scholar

via Electromyography as a Suitable Input for Virtual Reality-Based Biofeedback in Stroke Rehabilitation | SpringerLink

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[Abstract + References] Arm Games for Virtual Reality Based Post-stroke Rehabilitation – Conference paper

Abstract

Stroke is a leading cause of serious long-term disability. World Health Organization (WHO) published that the second leading of death is stroke accident and every year, 15 million people worldwide suffer from stroke attack, two-thirds of them have a permanent disability. Muscle impairment can be treated by intensive movements involving repetitive task, task-oriented and task-variegated. Conventional stroke rehabilitation is expensive, less engaging and at the same time need more time for the rehabilitation process and need more energy and time for the therapist to guide the stroke-survivor. Modern stroke rehabilitation is more promising and more effective with modern rehabilitation aids allowing the rehabilitation process to be faster, however, this therapist method can be obtained in the big cities. To cover the lack of rehabilitation process in this research will develop and improve post-stroke rehabilitation using games. This research using electromyography (EMG) device to analyze the muscle contraction during the rehabilitation process and using Kinect XBOX to record trajectory hands movements. Five games from movements sequence have designed and will be examined in this research. This games obtained two results, the first is the EMG signal and the second is trajectory data. EMG signal can recognize muscle contractions during playing game and the trajectory data can save the pattern of movements and showed the pattern to the monitor. EMG signal processing using time or frequency feature extractions is a good idea to obtain more information from muscle contractions, also velocity, similarities and error movements can be obtained by study the possible approaches.

References

  1. 1.
    Leading Cause of Death Malaysia: Stroke.: Retrieved from http://www.worldlifeexpectancy.com/malaysia-stroke (2017)
  2. 2.
    Mayo ayo Clinic Staff.: Stroke rehabilitation: what to expect as you recover. Retrieved from http://www.mayoclinic.org/stroke-rehabilitation/art-20045172 (2017)
  3. 3.
    Dobkin, B.H.: Strategies for stroke rehabilitation. Lancet Neurol. 3(9), 528–536 (2004)CrossRefGoogle Scholar
  4. 4.
    Riener, R., Frey, M., Bernhardt, M., Nef, T., Colombo, G.: Human-centered rehabilitation robotics. In: 9th International Conference on Rehabilitation Robotics, pp. 319–322 (2005)Google Scholar
  5. 5.
    Yeh, S., Lee, S., Wang, J., Chen, S., Chen, Y., Yang, Y., Hung, Y.: Virtual reality for post-stroke shoulder-arm motor rehabilitation : training system & assessment method. In: Paper Presented at 14th International Conference on e-Health Networking, Applications and Services, pp. 190–195. Beijing, China: IEEE (2012)Google Scholar
  6. 6.
    Yeh, S., Stewart, J., McLaughlin, M., Parsons, T., Winstein, C. J., Rizzo, A.: VR aided motor training for post-stroke rehabilitation: system design, clinical test, methodology for evaluation. In: Proceedings of the IEEE Virtual Reality Conference, pp. 299–300. Charlotte, USA (2007)Google Scholar
  7. 7.
    Prashun, P., Hadley, G., Gatzidis, C., Swain, I.: Investigating the trend of virtual reality-based stroke rehabilitation systems. In: Proceedings of the 14th International Conference Information Visualisation, pp. 641–647. London, UK (2010)Google Scholar
  8. 8.
    Trombetta, M., Henrique, M., Rogofski, B.: Motion Rehab AVE 3D: VR-based exergame for post stroke rehabilitation. J. Comput. Methods Progr. Biomed. 151, 15–20 (2017).  https://doi.org/10.1016/j.cmpb.2017.08.008CrossRefGoogle Scholar
  9. 9.
    Esfahlani, S., Bogdan, M., Alireza, S., George, W.: Validity of the Kinect and Myo armband in serious game for assessing upper-limb movement. J. Entertain. Comput. 27, 150–156 (2018).  https://doi.org/10.1016/j.entcom.2018.05.003CrossRefGoogle Scholar
  10. 10.
    Kutlu M., Freeman C., Ann-Marie H.: A home based FES system for upper-limb stroke rehabilitation with iterative learning control. J. Int. Fed. Autom. Control. Papers on-line 50(1), 12089–12094 (2016)CrossRefGoogle Scholar
  11. 11.
    Khairunizam, W., K., Suhaimi, R., Aswad, A.R.: Design of arm movement sequence for upper limb management after stroke. In: Proceedings of the International workshop on Nonlinier Circuits, Communications and Signal Processing. George Town, Malaysia (2015)Google Scholar
  12. 12.
    Sevgi, A., Ilkin, M., Oya, Umit Y., Sacide, S.: Virtual reality in upper extremity rehabilitation of stroke patients: a randomized controlled trial. J. Stroke cerebrovasc. Dis. 27(2), 3473–3478 (2018).  https://doi.org/10.1016/j.jstrokecerebrovasdis.2018.08.007CrossRefGoogle Scholar
  13. 13.
    Rash, G.S., EdD.: Electromyography fundamentals. https://www.researchgate.net/publication/265248133_Electromyography_Fundamentals (2002)
  14. 14.
    Kaewboon, W., Limsakul, C., Phukpattaranont, P.: Upper limbs rehabilitation system for stroke patient with biofeedback and force. In: Proceedings of the Biomedical Engineering International Conference. Amphur Muang, Thailand (2013)Google Scholar
  15. 15.
    Ritchie, H., Roser, M.: Causes of death. https://ourworldindata.org/causes-of-death (2017)
  16. 16.
  17. 17.
    Riener, R., Frey, M., Bernhardt, M., Nef, T., Colombo, G.: Human centered rehabilitation robotics. In: Proceedings of the 9th International Conference Rehabilitation Robotics, pp. 319–322. Chicago, USA (2005)Google Scholar
  18. 18.
    Ivey, F.M., Hafer-Macko, C.E., Macko, R.F.: Exercise rehabilitation after stroke. J. NeuroRx 3(4), 439–450 (2006).  https://doi.org/10.1016/j.nurx.2006.07.011CrossRefGoogle Scholar
  19. 19.
    Htoon, Z.L., Na’im, Sidek, S., Fatai, S.: Assessment of upper limb MUSCLE tone level based on estimated impedance parameters. In: Proceedings of the Conference on Biomedical Engineering and Sciences. Kuala Lumpur, Malaysia (2016)Google Scholar
  20. 20.
    Kleim, J.A., Jones, T.A.: Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. J. Speech Lang Hear Res. 51(1), 225–239 (2008).  https://doi.org/10.1044/1092-4388(2008/018)CrossRefGoogle Scholar
  21. 21.
    Takeuchi, N., Izumi, S.I.: Rehabilitation with post-stroke motor recovery: a review with a focus on neural plasticity. J. Stroke Res. Treat. (2013).  https://doi.org/10.1155/2013/128641CrossRefGoogle Scholar
  22. 22.
    Sveistrup, H.: Motor rehabilitation using virtual reality. J. NeuroEng. Rehabil. (2004).  https://doi.org/10.1186/1743-0003-1-10CrossRefGoogle Scholar
  23. 23.
    Langhorne, P., Bernhardt, J., Kwakkel, G.: Stroke rehabilitation. Lancet. J. Stroke Care 377(9778), 1693–1702 (2011).  https://doi.org/10.1016/S0140-6736(11)60325-5CrossRefGoogle Scholar
  24. 24.
    Suhaimi, R., Khairunizam, W., Ariffin, M.A.: Design of movement sequence for arm rehabilitation of post-stroke. In: Proceedings of the International Conference on Control System, Computing and Engineering. George Town, Malaysia (2015)Google Scholar
  25. 25.
    Suhaimi, R., Aswad, A.R., Adnan, N.H., Asyraf, F., Khairunizam, W., Hazry, D., Shahriman, A.B., Bakar, A., Razlan, Z.M.: Analysis of EMG-based muscles activity for stroke rehabilitation. In: Proceedings of the 2nd International Conference on Electronic Design (ICED), pp. 167–170. Penang, Malaysia (2014)Google Scholar
  26. 26.
    Basri, N.C., Khairunizam, W., Zunaidi, I., Bakar, S.A., Razlan, Z.M.: Investigation of upper limb movements for VR based post-stroke rehabilitation device. In: Proceedings of the 14th International Colloquium on signal processing and It’s Aplications (CSPA). Batu Feringghi, Malaysia (2018)Google Scholar
  27. 27.
    Majid, M.S.H., Khairunizam, W., Shahriman, A.B., Zunaidi, I.: EMG feature extraction for upper-limb functional movement during rehabilitation. In: Proceedings of the International Conference on Intelegent Informatics and Biomedical Science (ICIIMBS). Bangkok, Thailand (2018)Google Scholar
  28. 28.
    Majid, M.S., Khairunizam, W., Shahriman, A.B., Bakar, A.S., Zunaidi, I.: Performance evaluation of a VR-based arm rehabilitation using movements sequence patttern. In: Proceedings of the 14th International Colloquium on Signal Processing and It’s Aplications (CSPA). Batu Feringghi, Malaysia (2018)Google Scholar
  29. 29.
    Recommendation for sesor locations on individual muscles. Retrieved from http://seniam.org/sensor_location.htm

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[ARTICLE] FarMyo: A Serious Game for Hand and Wrist Rehabilitation Using a Low-Cost Electromyography Device – Full Text PDF

Abstract

One of the strategies used in recent years to increase the commitment and motivation of patients undergoing rehabilitation is the use of graphical systems, such as virtual environments and serious games. In addition to contributing to the motivation, these systems can simulate real life activities and provide means to measure and assess user performance. The use of natural interaction devices, originally conceived for the game market, has allowed the development of low cost and minimally invasive rehabilitation systems. With the advent of natural interaction devices based on electromyography, the user’s electromyographic data can also be used to build these systems. This paper shows the development of a serious game focused on aiding the rehabilitation process of patients with hand motor problems, targeting to solve problems related to cost, adaptability and patient motivation in this type of application. The game uses an electromyography device to recognize the gestures being performed by the user. A gesture recognition system was developed to detect new gestures, complementing the device’s own recognition system, which is responsible for interpreting the signals. An initial evaluation of the game was conducted with professional physiotherapists.

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[Abstract + References] Electromyographic indices of muscle fatigue of a severely paralyzed chronic stroke patient undergoing upper limb motor rehabilitation

Abstract

Modern approaches to motor rehabilitation of severe upper limb paralysis in chronic stroke decode movements from electromyography for controlling rehabilitation orthoses. Muscle fatigue is a phenomenon that influences these neurophysiological signals and may diminish the decoding quality. Characterization of these potential signal changes during movement patterns of rehabilitation training could therefore help improve the decoding accuracy. In the present work we investigated how electromyographic indices of muscle fatigue in the Deltoid Anterior muscle evolve during typical forward reaching movements of a rehabilitation training in healthy subjects and a stroke patient. We found that muscle fatigue in healthy subjects changed the neurophysiological signal. In the patient, however, no consistent change was observed over several sessions.
1. V. L. Feigin , B. Norrving , M. G. George , J. L. Foltz , A. Roth Gregory , and G. A. Mensah , “Prevention of stroke: a strategic global imperative,” Nat. Rev. Neurol., vol. 107, pp. 501–512, 2016.

2. A. Ramos-Murguialday et al , “Brain-machine interface in chronic stroke rehabilitation: a controlled study,” Ann. Neurol., vol. 74, no. 1, pp. 100–108, 2013.

3. A. Sarasola-Sanz et al , “A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients,” IEEE Int Conf Rehabil Robot, vol. 2017, pp. 895–900, Jul. 2017.

4. R. M. Enoka and J. Duchateau , “Muscle fatigue: what, why and how it influences muscle function,” J Physiol, vol. 586, no. 1, pp. 11–23, Jan. 2008.

5. M. González-Izal , A. Malanda , E. Gorostiaga , and M. Izquierdo , “Electromyographic models to assess muscle fatigue,” J. Electromyogr. Kinesiol., vol. 22, no. 4, pp. 501–512, Aug. 2012.

6. A. Sarasola Sanz et al , “EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies,” in 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), 2015.

7. P. V. Komi and P. Tesch , “EMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man,” Eur. J Appl Physiol, vol. 42, no. 1, pp. 41–50, Sep. 1979.

8. D. R. Rogers and D. T. MacIsaac , “A comparison of EMG-based muscle fatigue assessments during dynamic contractions,” J. Electromyogr. Kinesiol., vol. 23, no. 5, pp. 1004–1011, Oct. 2013.

9. B. Bigland-Ritchie , E. F. Donovan , and C. S. Roussos , “Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal efforts,” J Appl Physiol Respir Env. Exerc Physiol, vol. 51, no. 5, pp. 1300–1305, Nov. 1981.

10. G. V. Dimitrov , T. I. Arabadzhiev , K. N. Mileva , J. L. Bowtell , N. Crichton , and N. A. Dimitrova , “Muscle Fatigue during Dynamic Contractions Assessed by New Spectral Indices,” Med. Sci. Sports Exerc., 2006.

11. N. A. Riley and M. Bilodeau , “Changes in upper limb joint torque patterns and EMG signals with fatigue following a stroke,” Disabil Rehabil, vol. 24, no. 18, pp. 961–969, Dec. 2002.

12. M. J. Campbell , A. J. McComas , and F. Petito , “Physiological changes in ageing muscles,” J. Neurol. Neurosurg. Psychiatry, vol. 36, no. 2, pp. 174–182, 1973.

 

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[ARTICLE] Mechanics and energetics of post-stroke walking aided by a powered ankle exoskeleton with speed-adaptive myoelectric control – Full Text

Abstract

Background

Ankle exoskeletons offer a promising opportunity to offset mechanical deficits after stroke by applying the needed torque at the paretic ankle. Because joint torque is related to gait speed, it is important to consider the user’s gait speed when determining the magnitude of assistive joint torque. We developed and tested a novel exoskeleton controller for delivering propulsive assistance which modulates exoskeleton torque magnitude based on both soleus muscle activity and walking speed. The purpose of this research is to assess the impact of the resulting exoskeleton assistance on post-stroke walking performance across a range of walking speeds.

Methods

Six participants with stroke walked with and without assistance applied to a powered ankle exoskeleton on the paretic limb. Walking speed started at 60% of their comfortable overground speed and was increased each minute (n00, n01, n02, etc.). We measured lower limb joint and limb powers, metabolic cost of transport, paretic and non-paretic limb propulsion, and trailing limb angle.

Results

Exoskeleton assistance increased with walking speed, verifying the speed-adaptive nature of the controller. Both paretic ankle joint power and total limb power increased significantly with exoskeleton assistance at six walking speeds (n00, n01, n02, n03, n04, n05). Despite these joint- and limb-level benefits associated with exoskeleton assistance, no subject averaged metabolic benefits were evident when compared to the unassisted condition. Both paretic trailing limb angle and integrated anterior paretic ground reaction forces were reduced with assistance applied as compared to no assistance at four speeds (n00, n01, n02, n03).

Conclusions

Our results suggest that despite appropriate scaling of ankle assistance by the exoskeleton controller, suboptimal limb posture limited the conversion of exoskeleton assistance into forward propulsion. Future studies could include biofeedback or verbal cues to guide users into limb configurations that encourage the conversion of mechanical power at the ankle to forward propulsion.

Trial registration

N/A.

Background

Walking after a stroke is more metabolically expensive, leading to rapid exhaustion, limited mobility, and reduced physical activity [1]. Hemiparetic walking is slow and asymmetric compared to unimpaired gait. Preferred walking speeds following stroke range between < 0.2 m s− 1 and ~ 0.8 m s− 1 [2] compared to ~ 1.4 m s− 1 in unimpaired adults, and large interlimb asymmetry has been documented in ankle joint power output [34]. The ankle plantarflexors are responsible for up to 50% of the total positive work needed to maintain forward gait [56]; therefore, weakness of the paretic plantarflexors is especially debilitating, and as a result, the paretic ankle is often a specific target of stroke rehabilitation [78910]. In recent years, ankle exoskeletons have emerged as a technology capable of improving ankle power output by applying torque at the ankle joint during walking in clinical populations [78] and healthy controls [11121314]. Myoelectric exoskeletons offer a user-controlled approach to stroke rehabilitation by measuring and adapting to changes in the user’s soleus electromyography (EMG) when generating torque profiles applied at the ankle [15]. For example, a proportional myoelectric ankle exoskeleton was shown to increase the paretic plantarflexion moment for persons post-stroke walking at 75% of their comfortable overground (OVG) speed [8]; despite these improvements, assistance did not reduce the metabolic cost of walking or improve percent paretic propulsion. The authors suggested exoskeleton performance could be limited because the walking speed was restricted to a pace at which exoskeleton assistance was not needed.

Exoskeleton design for improved function following a stroke would benefit from understanding the interaction among exoskeleton assistance, changes in walking speed, and measured walking performance. Increases in walking speed post-stroke are associated with improvements in forward propulsion and propulsion symmetry [16], trailing limb posture [1718], step length symmetries [1719], and greater walking economies [1719]. This suggests that assistive technologies need to account for variability in walking speeds to further improve post-stroke walking outcomes. However, research to date has evaluated exoskeleton performance at only one walking speed, typically set to either the participant’s comfortable OVG speed or a speed below this value [78]. At constant speeds, ankle exoskeletons have been shown to improve total ankle power in both healthy controls [11] and persons post-stroke [8], suggesting the joint powers and joint power symmetries could be improved by exoskeleton technology. Additionally, an exosuit applying assistance to the ankle was able to improve paretic propulsion and metabolic cost in persons post-stroke walking at their comfortable OVG speed [7]. Assessing the impact of exoskeleton assistance on walking performance across a range of speeds is the next logical step toward developing exoskeleton intervention strategies targeted at improving walking performance and quality of life for millions of persons post-stroke.

In order to assess the impact of exoskeleton assistance across a range of walking speeds in persons post-stroke, we developed a novel, speed-adaptive exoskeleton controller that automatically modulates the magnitude of ankle torque with changes in walking speed and soleus EMG. We hypothesized that: 1) Our novel speed-adaptive controller will scale exoskeleton assistance with increases in walking speed as intended. 2) Exoskeleton assistance will lead to increases in total average net paretic ankle power and limb power at all walking speeds. 3) Exoskeleton assistance will lead to metabolic benefits associated with improved paretic average net ankle and limb powers.

Methods

Exoskeleton hardware

We implemented an exoskeleton emulator comprised of a powerful off-board actuation and control system, a flexible Bowden cable transmission, and a lightweight exoskeleton end effector [20]. The exoskeleton end effector includes shank and foot carbon fiber components custom fitted to participants and hinged at the ankle. The desired exoskeleton torque profile was applied by a benchtop motor (Baldor Electric Co, USA) to the carbon-fiber ankle exoskeleton through a Bowden-cable transmission system. An inline tensile load cell (DCE-2500 N, LCM Systems, Newport, UK) was used to confirm the force transmitted by the exoskeleton emulator during exoskeleton assistance.

Speed-adaptive proportional myoelectric exoskeleton controller

Our exoskeleton controller alters the timing and magnitude of assistance with the user’s soleus EMG signal and walking speed (Fig. 1). The exoskeleton torque is determined from Eq. 1, in which participant mass (mparticipant) is constant across speeds, treadmill speed (V) is measured in real-time, the speed gain (Gspeed) is constant for all subjects and across speeds, the adaptive gain (Gadp) is constant for a gait cycle and calculated anew for each gait cycle, and the force-gated and normalized EMG (EMGGRFgated) is a continuously changing variable.

τexo (t)=mparticipant×V×Gspeed×Gadp×EMGGRFgatedτexo (t)=mparticipant×V×Gspeed×Gadp×EMGGRFgated
(1)
Fig. 1
Fig. 1

Novel speed-adaptive myoelectric exoskeleton controller measures and adapts to users’ soleus EMG signal as well as their walking speed in order to generate the exoskeleton torque profile. Raw soleus EMG signal is filtered and rectified to create an EMG envelope, and the created EMG envelope is then gated by anterior GRFs to ensure assistance is only applied during forward propulsion. The adaptive EMG gain is calculated as a moving average of peak force-gated EMG from the last five paretic gait cycles. The pre-speed gain control signal is the product of the force-gated EMG and the adaptive EMG gain. The speed gain is determined using real-time walking speed and computed as 25% of the maximum biological plantarflexion torque at that given walking speed. Exoskeleton torque is the result of multiplying the speed gain with the pre-speed gain control signal

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[ARTICLE] Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks – Full Text

Abstract

Background

To assist people with disabilities, exoskeletons must be provided with human-robot interfaces and smart algorithms capable to identify the user’s movement intentions. Surface electromyographic (sEMG) signals could be suitable for this purpose, but their applicability in shared control schemes for real-time operation of assistive devices in daily-life activities is limited due to high inter-subject variability, which requires custom calibrations and training. Here, we developed a machine-learning-based algorithm for detecting the user’s motion intention based on electromyographic signals, and discussed its applicability for controlling an upper-limb exoskeleton for people with severe arm disabilities.

Methods

Ten healthy participants, sitting in front of a screen while wearing the exoskeleton, were asked to perform several reaching movements toward three LEDs, presented in a random order. EMG signals from seven upper-limb muscles were recorded. Data were analyzed offline and used to develop an algorithm that identifies the onset of the movement across two different events: moving from a resting position toward the LED (Go-forward), and going back to resting position (Gobackward). A set of subject-independent time-domain EMG features was selected according to information theory and their probability distributions corresponding to rest and movement phases were modeled by means of a two-component Gaussian Mixture Model (GMM). The detection of movement onset by two types of detectors was tested: the first type based on features extracted from single muscles, whereas the second from multiple muscles. Their performances in terms of sensitivity, specificity and latency were assessed for the two events with a leave one-subject out test method.

Results

The onset of movement was detected with a maximum sensitivity of 89.3% for Go-forward and 60.9% for Go-backward events. Best performances in terms of specificity were 96.2 and 94.3% respectively. For both events the algorithm was able to detect the onset before the actual movement, while computational load was compatible with real-time applications.

Conclusions

The detection performances and the low computational load make the proposed algorithm promising for the control of upper-limb exoskeletons in real-time applications. Fast initial calibration makes it also suitable for helping people with severe arm disabilities in performing assisted functional tasks.

Background

Exoskeletons are wearable robots exhibiting a close physical and cognitive interaction with the human users. Over the last years, several exoskeletons have been developed for different purposes, such as augmenting human strength [1], rehabilitating neurologically impaired individuals [2] or assisting people affected by many neuro-musculoskeletal disorders in activities of daily life [3]. For all these applications, the design of cognitive Human-Robot Interfaces (cHRIs) is paramount [4]; indeed, understanding the users’ intention allows to control the device with the final goal to facilitate the execution of the intended movement. The flow of information from the human user to the robot control unit is particularly crucial when exoskeletons are used to assist people with compromised movement capabilities (e.g. post-stroke or spinal-cord-injured people), by amplifying their movements with the goal to restore functions.

In recent years, different approaches have been pursued to design cHRIs, based on invasive and non-invasive approaches. Implantable electrodes, placed directly into the brain or other electrically excitable tissues, record signals directly from the peripheral or central nervous system or muscles, with high resolution and high precision [5]. Non-invasive approaches exploit different bio-signals: some examples are electroencephalography (EEG) [6], electrooculography (EOG) [7], and brain-machine interfaces (BMI) combining the two of them [8910]. In addition, a well-consolidated non-invasive approach is based on surface electromyography (sEMG) [11], which has been successfully used for controlling robotic prostheses and exoskeletons due to their inherent intuitiveness and effectiveness [121314]. Compared to EEG signals, sEMG signals are easy to be acquired and processed and provide effective information on the movement that the person is executing or about to start executing. Despite the above-mentioned advantages, the use of surface EMG signals still has several drawbacks, mainly related to their time-varying nature and the high inter-subject variability, due to differences in the activity level of the muscles and in their activation patterns [1115], which requires custom calibrations and specific training for each user [16]. For these reasons, notwithstanding the intuitiveness of EMG interfaces, it is still under discussion their efficacy and usability in shared human-machine control schemes for upper-limb exoskeletons. Furthermore, the need for significant signal processing can limit the use of EMG signals in on-line applications, for which fast detection is paramount. In this scenario, machine learning methods have been employed to recognize the EMG onset in real time, using different classifiers such as Support Vector Machines, Linear Discriminant Analysis, Hidden Markov Models, Neural Networks, Fuzzy Logic and others [151617]. In this process, a set of features is previously selected in time, frequency, or time-frequency domains [18]. Time-domain features extract information associated to signal amplitude in non-fatiguing contractions; when fatigue effects are predominant, frequency-domain features are more representative; finally, time-frequency domain features better elicit transient effects of muscular contractions. Before feeding the features into the classifier, dimensionality reduction is usually performed, to increase classification performances while reducing complexity [19]. The most common strategies for reduction are: i) feature projection, to map the set of features into a new set with reduced dimensionality (e.g., linear mapping through Principal Component Analysis); ii) feature selection, in which a subset of features is selected according to specific criteria, aimed at optimizing a chosen objective function. All the above-mentioned classification approaches ensure good performance under controlled laboratory conditions. Nevertheless, in order to be used effectively in real-life scenarios, smart algorithms must be developed, which are able to adapt to changes in the environmental conditions and intra-subject variability (e.g. changes of background noise level of the EMG signals), as well as to the inter-subject variability [20].

In this paper, we exploited a cHRI combining sEMG and an upper-limb robotic exoskeleton, to fast detect the users’ motion intention. We implemented offline an unsupervised machine-learning algorithm, using a set of subject-independent time-domain EMG features, selected according to information theory. The probability distributions of rest and movement phases of the set of features were modelled by means of a two-component Gaussian Mixture Model (GMM). The algorithm simulates an online application and implements a sequential method to adapt GMM parameters during the testing phase, in order to deal with changes of background noise levels during the experiment, or fluctuations in EMG peak amplitudes due to muscle adaptation or fatigue. Features were extracted from two different signal sources, namely onset detectors, which were tested offline and their performance in terms of sensitivity (or true positive rate), specificity (or true negative rate) and latency (delay on onset detection) were assessed for two different events, i.e. two transitions from rest to movement phases at different initial conditions. The two events were selected in order to replicate a possible application scenario of the proposed system. Based on the results we obtained, we discussed the applicability of the algorithm to the control of an upper-limb exoskeleton used as an assistive device for people with severe arm disabilities.

Materials and methods

Experimental setup

The experimental setup includes: (i) an upper-limb powered exoskeleton (NESM), (ii) a visual interface, and (iii) a commercial EMG recording system (TeleMyo 2400R, Noraxon Inc., AZ, US).

NESM upper-limb exoskeleton

NESM (Fig. 1a) is a shoulder-elbow powered exoskeleton designed for the mobilization of the right upper limb [2122], developed at The BioRobotics Institute of Scuola Superiore Sant’Anna (Italy). The exoskeleton mechanical structure hangs from a standing structure and comprises four active and eight passive degrees of freedom (DOFs), along with different mechanisms for size regulations to improve comfort and wearability of the device.
Fig. 1

Fig. 1a Experimental setup, comprising NESM, EMG electrodes and the visual interface; b Location of the electrodes for EMG acquisition; c Timing and sequence of action performed by the user during a single trial

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