Article

The Influence of EMG-Triggered Robotic Movement
on Walking, Muscle Force and Spasticity
after an Ischemic Stroke
Patrycja Lewandowska-Sroka 1, Rafał Stabrawa 1, Dominika Kozak 2,*, Anna Poświata 2, Barbara Łysoń-Uklańska 3,
Katarzyna Bienias 3, Anna Roksela 4,, Marcin Kliś 4 and Michał Mikulski 2
Faculty of Rehabilitation, State Higher Vocational School in Nowy Sacz, 33-300 Nowy Sącz, Poland;p.lewandowska-sroka@o2.pl (P.L.-S.); stabrawa@gmail.com (R.S.)
2 Clinical Department, Egzotech sp. z o.o., 44-100 Gliwice, Poland; anna.poswiata@egzotech.com (A.P.);
michal.mikulski@egzotech.com (M.M.)
3 Faculty of Rehabilitation, The Jozef Pilsudski University of Physical Education, 00-968 Warsaw, Poland;
domuss@op.pl (B.Ł.-U.); katarzyna.bienias@gmail.com (K.B.)
4 Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology,
44-100 Gliwice, Poland; Anna.Roksela@polsl.pl (A.R.); m.klis@vp.pl (M.K.)* Correspondence: dominika.kozak@egzotech.com; Tel.: +48-790540086
1

Citation: Lewandowska-Sroka, P.;
Stabrawa, R.; Kozak, D.; Poświata,A.; Łysoń-Uklańska, B.; Bienias, K.;
Roksela, A.; Kliś, M.; Mikulski, M.
The Influence of EMG-Triggered
Robotic Movement on Walking,Muscle Force and Spasticity after an
Ischemic Stroke. Medicina 2021, 57,
227. https://doi.org/10.3390/medicina
57030227
Academic Editors: Francisco Purroy
Received: 13 January 2021
Accepted: 25 February 2021
Published: 2 March 2021
Publisher’s Note: MDPI stays neutral with regard to jurisdictional
claims in published maps and insti-

Abstract: Background and Objectives: Application of the EMG-driven robotic training in everyday
therapeutic processes is a modern and innovative form of neurorehabilitation among patients after
stroke. Active participation of the patient contributes to significantly higher activation of the sensorimotor network during active motor control rather than during passive movement. The study
objective was to determine the effect of electromyographic triggering (EMG-triggered) robotic rehabilitation device treatment on walking, muscle force, and spasticity after an ischemic stroke. Materials and Methods: A total of 60 participants with impaired motor function and gait after subacutestroke were included in the study. Each patient was randomly assigned to an intervention or control
group (IG or CG). All patients, except standard therapy, underwent 1 additional session of therapy
per day, 5 days a week for 6 weeks. IG had 30 min of training on the robot, while CG received
exercises on the lower limb rotor. The subjects were assessed with Timed Up and Go Test (TUG),
Ashworth scale, knee range of motion (ROM), Lovett Scale, and tight circumference at baseline andat weeks 2, 4, and 6. Results: For seven parameters, the values credibly increased between consecutive measurements, and for the Ashworth scale, they credibly decreased. The biggest changes wereobserved for the measurements made with Lovett scale. The average thigh circumference as measured 5 and 15 cm above the knee increased credibly more in the robot condition, as compared tocontrol condition. Additionally, the decrease in Ashworth values over time, although statisticallycredible in both groups, was credibly higher in the robot condition. Conclusion: The inclusion of the
EMG-triggered neurorehabilitation robot in the patient’s daily rehabilitation plan has a positive effect on outcomes of the treatment. Both proposed rehabilitation protocols significantly improved
patients’ condition regarding all measured outcomes, but the spasticity and thigh circumference
improved significantly better in the robotic group in comparison to controls.

tutional affiliations.

Keywords: stroke; rehabilitation; EMG-triggered; rehabilitation robot; spasticity; mobility; gait

Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

1. Introduction
Stroke is currently one of the most important health problems in the adult populationworldwide, for both medical and social reasons. Almost one third of deaths in the world
are caused by strokes [1]. Motor skills are one of the most important areas affected bystroke. Patients may experience a variety of disabilities in different body parts andMedicina 2021, 57, 227. https://doi.org/10.3390/medicina57030227

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2 of 11different hemispheres can be affected. The most common impairments of the early stagesof stroke are weakness and paresis, which may lead to a learned nonuse of limbs. Sensory
impairments, chronic pain, and immobility of the patient in the early stages after stroke
may also contribute to a learned nonuse state [2]. Motor skills impairments are most associated with the reduction in functionality [3]. After stroke, normal muscle activity tone
can be also disrupted by neuronal damage. It leads to inappropriate decrease or increaseof muscle activity. Spasticity, which is a common issue associated with a stroke, is abnormal muscle tone recognized clinically as resistance to passive muscle stretch, which increases with velocity of stretch. In patients with no functionally useful voluntary limb
movement, spasticity can maintain an abnormal resting limb posture leading to contracture formation. In patients with functionally useful voluntary limb movement, inappropriate co-activation of agonist and antagonist muscles can impede normal limb movement[4]. On the other hand, some of the neuronal pathways may remain unaffected after the
stroke [5].
Electromyography (EMG)-triggered therapeutic programs create an interaction between the neuromuscular system and robotic device. The electromyographic techniqueprovides more complete information about the patient’s current muscular state (one muscle or the entire muscle group). During rest, bioelectric activity is at the lowest point, andthe EMG record shows the degree of muscle stimulation based on the recording of theamplitude and frequency of biopotentials. The main parameters of biopotentials are used
as an indicator of relaxation or a state of excitation of specific muscles. Mostly, the devices
with at least two channels are used to be able to apply therapy that involves working on
antagonist muscles. The patient’s task during the therapeutic session is to control muscle
tone for performance with the conditions of the gym, neither using the resistance of thegiven device, nor in individual work with therapists [6].
Application of the EMG-driven robotic training in everyday therapeutic processes isa modern and innovative form of neurorehabilitation among patients after stroke. Rehabilitation robots with the use of reactive electromyography capture the EMG signal froma patient’s muscles and, on this basis, it assists with a given move. Active participation of
the patient contributes to significantly higher activation of the sensorimotor network during active motor control rather than during movement performed passively [7]. Using
EMG triggered therapy in rehabilitation of stroke patients provides a possibility to learn
how to use these preserved pathways. After a treatment, a patient is able to better control
the muscle tone. This accelerates the motor function recovery, which allows for regaining
the functional efficiency after a stroke. In addition, the literature proves that the key period in terms of regaining the functional capacity of the body is the first three months after
the cerebrovascular incident [7].
Luna EMG provides an electromyographic triggered robot-assisted therapy of upperand lower limbs. In our study, the device was used in patients after stroke to work on thelower limb movement. Luna EMG were detected the intentions of a stroke survivor bymeans of surface electromyography (EMG) signals located on the hemiplegic side of thelower limb and assisted in the activities of straightening and flexing the knee joint. In addition, while delivering robot-assisted therapy tailored to the individual patient, the system can record signals that may be useful for better understanding stroke recovery [8].
The aim of the study was to determine the effect of electromyographic triggering(EMG-triggered) robotic rehabilitation device treatment on walking, muscle force, andspasticity after an ischemic stroke.
2. Materials and Methods
2.1. Design Overview
A prospective, randomized controlled two-arm trial design was used: group (1)standard physiotherapy and robotic assisted exercises, and group (2) standard

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physiotherapy and lower limb rotor exercises. All testing was carried out by the first author who was not blind to participant allocation.
2.2. Setting and Participants
Ethical Approval was granted by Bioethics Committee by the District Medical Chamber in Cracow (NR 10/KBL /OIL/2019). All testing took place at “RehStab” Rehabilitation
Clinic in Limanowa, Poland. The inclusion criteria were as follows: ischemic stroke, notlater than 6 months ago; muscle strength of extensors and knee flexors on the Lovett scale
below 3; functional disorders of the lower limb; patient’s condition allows full understanding of commands; and continued/uninterrupted rehabilitation process for 42 days.
The exclusion criteria included: cognitive impairment—lack of or poor cooperation between the patient and the therapist; stroke (more than 6 months after the incident); unstable clinical condition; muscle strength of knee extensors and flexors on the Lovett scale
greater than or equal to 3; rigid fixed contractures within the lower limb; and significant
spasticity (Ashworth scale of 3 and above).
2.3. Randomization and Interventions
After written informed consent, demographic information and baseline outcome
data had been collected prior to participants getting randomly allocated to a group by asimple randomization. Both intervention groups were then introduced to the allocatedprogram, undertaking each of the program-specific exercises. Both groups participants
undertook six weeks of allocated exercise, five times per week, for 90–120 min each session, depending on the patient’s condition. The study participants were divided into twogroups: intervention group (A, robot group) and control group (B, control group). In theintervention group (30 patients), rehabilitation was based on individual standard physiotherapy and rehabilitation robot Luna EMG. In the control group (30 patients), rehabilitation was based on individual standard physiotherapy and a use of a lower limb rotor (30min).
Standard physiotherapy program included:
1.

2.
3.

Individual kinesiotherapy (passive and assisted exercises of upper and lower paretic
limb, active exercises focused on balance and coordination, breathing exercises, education, or improving gait),
Physical therapy (laser therapy, phototherapy, ultrasound, hydrotherapy, pressotherapy), andClassical lower limbs massage.

Luna EMG is a rehabilitation robot specifically designed to aid with the physiotherapy of neurological patients suffering from clinical weakness. It is intended as an all-inone platform for complex personalized therapy for patients suffering from neurologicalconditions. It tackles the key problems such as muscle weakness, mobility disorders, gaitproblems, and range of motion restrictions specifically by automating the process of personalized, motivating physiotherapy based on electromyography, force, and position
sensing.
The patient is connected to Luna EMG through extensions—exchangeable mechanical parts that are connected to the patient by straps or by grip. Movement is controlled bya Windows Application from a mobile therapist panel providing a User Interface (UI),
patient management, reporting and internet connectivity for the purpose of remote diagnostics and oversight. The device allows us to increase patient muscle force through isokinetic, isotonic, and isometric exercises. The innovative technology “EMG-triggered robotic movement” allows us to work actively with clinically weak patients, even where no
movement is visible. Luna EMG detects EMG activity of the muscle and based on that, it
provides assistance during the movement. If no movement or activity is present, the device provides passive assistance.

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The Luna EMG exercise protocol, which was a combination of CPM (continuous passive movement) with reactive electromyography exercises, consisted of: 5 min of CPM of the knee flexion/extension.
 10 min of EMG-triggered exercise, using the rectus femoris EMG activity to activate
the assistance of the device towards extension.
 10 min of EMG-triggered exercise, using the biceps femoris EMG activity to activate
the assistance of the device towards flexion.
 5 min of CPM of the knee.
Exercises with Luna EMG were conducted in sitting position. Therapeutic procedures were adapted according to the functional state of every person and adjusted individually. All exercises were completed on a one-to-one basis with the first author supervising the sessions.
2.4. Outcome Measurements
For the patient’s assessment, several measurement tools were used:--

Timed Up and Go test (TUG): the test is performed from a sitting position. The patient on command gets up from the chair to walk a distance of 3 m, then turns backand sits on the chair at the starting point.
Ashworth scale (muscle tension examined by the therapist): measurement of resistance during passive knee flexion and extension.
Tight circumference: 5 cm and 15 cm above the patella.
Lovett scale assessment for rectus femoris and semimembranosus muscle.
Range of motion (ROM): knee flexion.

The subjects were assessed with TUG, Ashworth scale, ROM, Lovett Scale, and tight
circumference at baseline and at weeks 2, 4, and 6.
3. Results
The group covered 45.5% of all 132 patients after stroke who underwent the rehabilitation treatment at the clinic at that time. Sixty-one patients did not meet the inclusion
criteria. The total number of patients participating in the study was 71. Eleven patients
have been disqualified during the study (3-absenteeism over 10% of training, 5—shorter
than 4 weeks, 3—another stroke episode during the research). Sixty participants (27 female) took part in the study. Half of them were assigned to the control group (15 female),and other half to the treatment (robot) group (12 female). Data from the last measurement(sixth week) were not available for 4 participants in the control group and for 8 participants in the treatment group. All 60 patients participated in a 6-week rehabilitation program.
The mean age of patients at the time of research was 66.8 years ± 11.5. The oldest
patient was 91 years old, while the youngest was 29. Right-hand side manifestation of thecerebral stroke was observed in 31 (51.7%) cases. Left-hand side paresis occurred in 29(48.3%) cases.
Data were analyzed using R 4.0.2 statistical software [9]. Parameters distributions,separately for each experimental condition and measurement time, are presented graphically in Figure 1. Many of the distributions are asymmetrical and numerous deviant observations can be clearly noted in the data. For this reason, to summarize the data medians
and median, absolute deviations as measures of central tendency and dispersion are presented in Table 1.

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Figure 1. Distributions of parameters separately for training conditions and consecutive measurements (m—meters, ROM—range of motion, circ.—circumference).
Table 1. Descriptive statistics.

Parameter
Time up and go
10 m walk
ROM knee
Thigh circ. (5 cm)
Thigh circ. (15 cm)
Rectus femorismuscle (LOVET)
Semimembranosus
muscle (LOVET)ASHWORTH

Condition
Control
Robot
Control
Robot
Control
Robot
Control
Robot
Control
Robot
Control
Robot
Control
Robot
Control
Robot

Week
Zero
II
IV
VI
0 (0)
20.5 (20.02)
19 (14.83)
18 (17.79)
9 (13.34)
16 (11.86)
15.5 (9.64)
17.5 (12.6)
0 (0)17 (19.27)
19 (14.08)
18.5 (12.6)
8.5 (12.6)
11.5 (9.64)
12 (7.41)
12.5 (8.15)
110 (22.24) 117.5 (11.12) 120 (14.83) 122.5 (11.12)
115 (14.83) 120 (14.83)
125 (7.41)
127.5 (3.71)
41 (2.97)
41 (3.71)
41.25 (4.08) 41.75 (3.34)
40.25 (4.45)
41 (4.45)
40.75 (5.56)
42 (4.08)
47 (3.34)
48.5 (5.19) 48.25 (4.45) 48.75 (3.34)
46.5 (5.19)
47 (5.19)
46.75 (5.93)
49 (6.67)
2.75 (0)3 (0.37)
3.25 (0.74)
3.5 (0.74)
2.75 (0)3.25 (0.19)
3.25 (0.74)
4 (0.37)
2.75 (0)3 (0.37)
3.25 (0.56)
3.5 (0.74)
2.75 (0)3.25 (0.37)
3.25 (0.74)
3.88 (0.37)
1 (0)
1 (0)
1 (0)
1 (0)
1 (0)
1 (0)
1 (0.37)
1 (1.48)3.1. Modeling of the Treatment EffectTo adequately measure the effects of training in both conditions, Bayesian skew-normal multilevel regression with participant specific intercept was used for each parameter.
The model allows control for the skewed distributions, unequal numbers of observations
per participant without data loss, and for differences in average values of a parameter
between participants.

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Prior to modeling, the parameters were transformed into Z-scores. Training wascoded orthogonally with Robot condition as −1 and Control as 1. The effect of training
represents the difference in overall means between conditions. The effect of measurement(week) was treated as an ordered factor and coded using the monotonic effect [10]. Thiseffect assumes that change in a parameter is either monotonically increasing or decreasing(note that the increase/decrease can be nonlinear). Based on the descriptive statistics (Table 1, Figure 1), this is a reasonable assumption for the week effect. Finally, interaction oftraining and week effects was included. This effect is the most important as it represents
the difference in the increase/decrease rate of a parameter between experimental conditions.
In Bayesian statistics, the inference is based on analyzing posterior probability distributions of a model parameters, obtained by integrating likelihood (data) with prior probability distributions. The parameter (e.g., training effect) is said to be statistically credible
when 95% credibility intervals (CI) of the posterior distribution exclude zero [11], as apoint estimate of the effect means of the posterior distributions are presented.
To approximate posterior distributions of the models, the Markov Chain Monte Carlo(MCMC) sampling procedure was conducted using the brms package [12]. For each reported model, six parallel MCMC chains were used. Each chain consisted of 10,000 samples, with 5000 samples used as a warm up period and every 10th sample recorded, resulting in 3000 recorded samples in total. the sampling procedure was efficient and resulted in well mixed and not autocorrelated chains and unimodal posteriors.
3.2. Treatment Effects
Results of the models are presented in Table 2 and posterior predictive means foreach treatment condition and measurement are in Figure 2. As a measure of the overallmodel for Bayesian, the R2 is reported [13]. First, the groups did not differ in respect to theoverall means of parameters as indicated by absence of credible parameters of treatment
effects. Second, for all parameters, the main effect of week was observed, indicating thatfor seven parameters, the values credibly increased between consecutive measurements,and for the Ashworth, the scale credibly decreased. The biggest changes were observed
for the measurements made with the Lovett scale.
Table 2. Results of multilevel skew-normal regressions.
Population-Level Effects
Ind.-Level EffectTraining
Week
Tr.:Week
τ
σ
−0.02
0.09
0.01
0.06
0.8
Time up and go(−0.12, 0.08)(0.03, 0.16)(−0.05, 0.06)(0, 0.14)(0.73, 0.89)
−0.02
0.08
0.01
0.04
0.75
10 m walk(0–0.12, 0.08)(0.03, 0.14)(−0.03, 0.07)(0, 0.11)(0.68, 0.82)
−0.16
0.22
0
0.81
0.45
ROM knee(−0.37, 0.07)(0.17, 0.28)(−0.05, 0.05)(0.66, 0.98)(0.4, 0.51)
0.14
0.03
−0.03
1.01
0.17
Thigh circ. (5 cm)(−0.12, 0.4)
(0.01, 0.05)(−0.05, −0.01)(0.85, 1.22)(0.15, 0.19)
0.18
0.02
−0.03
1.01
0.17
Thigh circ. (15 cm)(−0.08, 0.45)(0, 0.04)(−0.05, −0.01)(0.85, 1.22)(0.16, 0.2)
Rectus femoris
−0.1
0.51
−0.02
0.75
0.38
muscle (LOVET)(−0.3, 0.1)
(0.46, 0.55)(−0.06, 0.03)(0.62, 0.91)(0.34, 0.42)
Semimembranosus
−0.1
0.52
−0.01
0.73
0.39
muscle (LOVET) (−0.3, 0.11)(0.47, 0.57)(−0.06, 0.03)(0.6, 0.89)(0.35, 0.44)
0.1
−0.19
0.07
0.86
0.45
ASHWORTH(−0.15, 0.34) (−0.26, −0.13)(0.01, 0.12)(0.72, 1.06)(0.41, 0.51)
Credible parameters of the treatment effects are presented in bold.
Parameter

ModelαBayesian R2
11.72
0.01
(7.76, 16.71)(0, 0.04)
12
0.01(8.07, 16.69)(0, 0.03)
−5.35
0.09
(−9.89, −2.52) (0.05, 0.17)
−5.61
0.03
(−10.97, −0.53)(0, 0.11)
−6.91
0.03
(−11.93, −3.4)(0, 0.13)
−2.84
0.32
(−7.37, 0.57)(0.28, 0.37)
−1.26
0.35
(−4.54, 1.24)(0.3, 0.4)
−1.97
0.1
(−8.53, 2.21)(0.04, 0.19)

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Finally, three credible interactions were observed. The average thigh circumference
as measured 5 and 15 cm above the knee increased credibly more in the robot condition,
as compared to control condition. In the latter, the average circumference was constantover the consecutive measurements. Additionally, the decrease in Ashworth values overtime, although statistically credible in both groups, was credibly higher in the robot condition.

Figure 2. Posterior predictive means (points) with 95% credibility intervals (vertical lines).

In summary, the results indicate that training in both conditions worked, but thetraining with the robot resulted additionally in a slight but stable increase of thigh circumference and moderately higher decrease in spasticity over time.
4. Discussion
The aim of the study was to determine the impact of using advanced technology incombination with conventional therapy on the stroke survivors’ rehabilitation process
and its effect on aspects like spasticity, muscle force, or walking.
EMG-triggered assistive robotic training of neurological patients is an innovative
therapeutic approach, which is based on the principles of the usage of robots in rehabilitation. It ensures intensive, repetitive and task-oriented work under the supervision orwith the help of a therapist [14]. Research shows that post-stroke motor recovery depends
on active rehabilitation by voluntary participation of the patient’s paretic motor system as
early as possible in order to promote reorganization of the brain. EMG-based robots havebeen developed, since voluntary residual motor efforts, to the affected limb, have not been
involved enough in most robot-assisted rehabilitation for patients after stroke.
Luna EMG is using the above-mentioned approach and in our study was used towork on the paretic lower limb with patients after a stroke. Currently, there are studies
available in the literature using EMG-triggered training in post-stroke patients, but mostly
for the upper limb [15]. In addition, there are several types of robotic technology interventions, as multi-joint exoskeletons, to the ones, which are aimed at single joint therapy.
Luna EMG robotic device, which was used as an intervention tool for our study, gives aMedicina 2021, 57, 227

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lot of opportunities to train different joints, both in upper and lower limb, but in one axis
and has an EMG-triggered assistance, while most of the scientific works are concernedwith EMG-triggered resistance training.
First of all, by looking into the Milot et al. study [16], where they evaluated whether
multi-joint functional robotic training would translate into greater gains in arm function
than single joint robotic training, we see that no significant difference was noted between
multi joint functional and single joint robotic training programs. This challenges the ideathat multi joint functional robotic training is superior to single joint robotic training.
Based on the results of our research, we looked more deeply into the influence of
EMG-triggered assistance robotic training technology in combination with conventional
therapy on spasticity, muscle force, and walking.
In our study, both groups have achieved the decrease of the knee spasticity, but theintervention group had significantly higher reduction, compared to the control group. Exercises that promote rapid launch have a positive effect on reducing muscle tone after
damage of the central nervous system, while little or no mobilization leads to contractures
and an increase in spasticity [17]. The same results were shown in the review of Bertani etal., where 14 randomized controlled clinical trials, two systematic reviews, and one metaanalysis were included to assess effects of robot-assisted upper limb rehabilitation in
stroke patients. A modified Ashworth scale was also selected for measuring muscle tone.
According to those findings, upper limb rehabilitation using a robot was more effective in
improving the restoration of motor functions of the upper limb and was also associatedwith a decrease in spasticity within the exercised limb in patients with chronic stroke, in
relation to conventional therapy. Despite the improvement, no statistical significance wasobserved, where in our study, this significant difference was noted [18].
Hu et al.’s [19] work is another example of the effective use of robots based on the
EMG signal as primary input on spasticity. Researchers evaluated an electromyography(EMG)-driven hand robot developed for post-stroke rehabilitation training. All subjects
attended a 20-session training (3–5 times/week), by using the hand robot to practice object
grasp/release and arm transportation tasks. Significant reduction in spasticity of the fingers was measured and shown through the Modified Ashworth Scale (p < 0.05). In anotherstudy of Hu et al. [20], where they investigated the training effects of treatments on thewrist joint of subjects with chronic stroke with an interactive electromyography-driven
robot and a robot with continuous passive motion, the interactive treatment reduced spasticity, but also improved muscle coordination. The passive mode training reduced spasticity only in the wrist flexor, but the effect was not long lasting.
Song et al. [21] were assessing the myoelectrically controlled robotic system with 1
degree-of-freedom, developed to assist elbow training in a horizontal plane with intention
involvement for people after stroke. The system could provide continuous assistance inextension torque, which was proportional to the amplitude of the subject’s electromyographic (EMG) signal from the triceps, and could provide resistive torques during movement. After 20 sessions of training, there were also statistically significant improvementsin the modified Ashworth scale. The improvement was also noted for the Fugl-Meyer
scale for shoulder and elbow, motor status scale, elbow extension range, muscle strength,
and root mean square error between actual elbow angle and target angle.
Strength training is commonly considered to be a progressive resistance exercise.
However, it should be remembered that any intervention that involves attempted repetitive effortful muscle contractions can result in increased motor unit activity and increase
strength [22]. We have analyzed both groups results in that perspective as well. The muscle force of the knee joint was significantly higher in both groups after the rehabilitation
process, but the average thigh circumference as measured 5 and 15 cm above the kneeincreased credibly more after rehabilitation protocol with robotic training included.
Anwer et al. have shown that the combination of isometric exercises with the use of
biofeedback increases the isometric strength of the muscle when undertaking training for
5 weeks. Muscle strength in the research group was 23% greater than in the control groupMedicina 2021, 57, 227

9 of 11at the end of fifth week [23]. The experimental group, which received auditory and visual
EMG feedback (which was present also in our training protocol) while exercising, demonstrated significantly greater strength gains than the control group. This shows that it isworth including biofeedback in the patient’s daily exercise, remembering that neurological rehabilitation is a long-term process.
The Son et al. [24] study aims to investigate the idea that an active-resistive training
with an EMG-based exoskeleton robot could be beneficial to muscle strength and antagonist muscle co-contraction control after 4-week intensive elbow flexion/extension training.
As a result, there was no significant difference in the maximum flexion or extension torqueat pre- and post-training. However, the co-contraction ratio of the triceps brachii muscle
as the antagonist was significantly decreased after the 4-week training. The active-resistive training with the exoskeleton robot in the older people yielded promising results,
showing significant changes in the antagonist muscle co-contraction.
The objective of Song et al.’s [25] study was to evaluate the feasibility of robot-assisted rehabilitation using myoelectric control on upper limb motor recovery. As a result,with the myoelectrically controlled assistive torque, stroke survivors could reach a larger
range of motion with a significant decrease in the EMG signal from the agonist muscles.
The stroke survivors could be trained in the unreached range with their voluntary residual
EMG on the paretic side. After a 20-session rehabilitation training, there was a non-significant increase in the range of motion and a significant decrease in the root mean squareerror (RMSE) between the actual wrist angle and target angle. Significant improvements
also could be found in muscle strength and clinical scales. They concluded that these results indicate that robot-aided therapy with voluntary participation of the patient’s paretic
motor system using myoelectric control might have a positive effect on upper limb motor
recovery. Basteris et al. [26] also confirmed in their systematic review that stressing active
contribution by the patients, e.g., through EMG-modulated forces, may be beneficial to
stroke patients. We have measured the ROM of the knee as well, and in our study, bothgroups have improved significantly in that matter.
Studies carried out by Tsaih et al. [27], in patients with chronic stroke, have confirmedthat the use of biofeedback in combination with ankle exercises improved the strength
during 6 weeks of muscle exercises. Despite those results, there was no statistically significant improvement in the TUG tests. In our study, where we were focusing on the knee
joint in particular, there was improvement of TUG results in both groups, but without any
significant difference between them.
A systematic review of robot interventions for gait function improvement in patientswith subacute stroke by Ji-Eun Cho et al. showed that some studies indicate a significant
difference between the control group and the experimental group that performed generalgait training, but other studies did not [28]. Therefore, there is still no clear answer
whether the training with the use of rehabilitation robots is more effective for improving
gait overall. However, the use of neurorehabilitation robots has its advantages because it
reduces the physical load of physiotherapists, allowing them to work with several patientsat the same time, which ensures greater throughput and availability of rehabilitation.
EMG-triggered rehabilitation robots can be dedicated to patients who are not able tofully engage in training performed especially at the beginning of the rehabilitation process
without the constant help of a physiotherapist. Rehabilitation robots reduce the therapist’s
involvement and reduce his physical load, ensuring constant and repetitive training.
These benefits have a significant impact on the performance of the physiotherapist’s workin terms of the quality-of-care services [28].
Activation of EMG between antagonist and a pair of muscle agonists around the joint
can provide the effect of damping the joint during movement, which in turn can contribute to the accuracy of motion in dynamics movement [29]. Unfortunately, due to a damage
caused by stroke, abnormal muscle activation patterns are often observed after it. Movements supported by non-paretic limbs may not be energy efficient, which is directly thecase associated with a deterioration in both accuracy and traffic efficiency [30].

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5. Conclusions
This study indicates that the inclusion of the EMG-triggered neurorehabilitation robot in the patient’s daily rehabilitation plan has a positive effect on outcomes of the treatment. Both proposed rehabilitation protocols significantly improved patients’ condition
regarding all measured outcomes, but the spasticity and thigh circumference improved
significantly better in the robotic group in comparison to controls.
Further studies with more entities are needed to create more specific recommendations and protocols in regard to the usage of the EMG-triggered robotic training approachin patients after a stroke.
Author Contributions: Methodology, M.K., A.R.; Conceptualization, M.M.; Supervision, R.S.; Data
curation, B.Ł.-U.; Writing—Original draft P.L.-S., D.K.; Writing—Review and editing A.P., K.B.; All
authors have read and agreed to the published version of the manuscript.
Funding: This study was supported by the National Center for Research and Development of Poland under Grant No. POIR.01.01.01-00-2077/15. EGZOTech company was a sponsor for the study
and this fact may be considered as a possible conflict of interest. Nevertheless employees of EGZOTech company (Dominika Kozak, Anna Poświata, Michał Mikulski) were not included in direct
activi-ties related to study protocol (patient’s recruitment, data collection, results analysis) but only
sup-ported the researchers by providing consultations regarding robotic technology that EGZOTech developed as a company and provided the equipment for the purpose of this study.
Institutional Review Board Statement: The project was approved by the Bioethics Committee byDistrict Medical Chamber in Cracow (NR 10/KBL /OIL/2019) on 22 January 2019. The study is registered at ClinicalTrials.gov: NCT04497545.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: Not applicable.
Conflicts of Interest: Patrycja Lewandowska Sroka, Rafał Stabrawa, Anna Roksela, Barbara ŁysońUklańska, Katarzyna Bienias, Marcin Kliś declare that they have no conflict of interest.

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