International Journal ofEnvironmental Researchand Public Health

ArticleApplication of an EMG-Rehabilitation Robot in Patients with
Post-Coronavirus Fatigue Syndrome (COVID-19)—A
Feasibility Study
Ewa Zasadzka 1 , Sławomir Tobis 1 , Tomasz Trzmiel 1, * , Renata Marchewka 2 , Dominika Kozak 3,4 ,Anna Roksela 4,5 , Anna Pieczyńska 1 and Katarzyna Hojan 1,2,6
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2
3
4
5

6

*

Citation: Zasadzka, E.; Tobis, S.;
Trzmiel, T.; Marchewka, R.; Kozak,
D.; Roksela, A.; Pieczyńska, A.;
Hojan, K. Application of an
EMG-Rehabilitation Robot in Patientswith Post-Coronavirus Fatigue
Syndrome (COVID-19)—A Feasibility
Study. Int. J. Environ. Res. Public
Health 2022, 19, 10398. https://doi.org/10.3390/ijerph191610398
Academic Editor: Paul B.

Department of Occupational Therapy, Poznan University of Medical Sciences, 60-781 Poznan, Poland
Neurorehabilitation Ward, Greater Poland Provincial Hospital, 60-480 Poznan, Poland
Department of Physiotherapy, University of Health Science, 85-067 Bydgoszcz, Poland
Egzotech sp. z o.o., 44-100 Gliwice, Poland
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology,
44-100 Gliwice, Poland
Department of Rehabilitation, Greater Poland Cancer Centre, 61-866 Poznan, Poland
Correspondence: ttrzmiel@ump.edu.pl

Abstract: This pilot study aimed to assess the safety and feasibility of an EMG-driven rehabilitation
robot in patients with Post-Viral Fatigue (PVF) syndrome after COVID-19. The participants wererandomly assigned to two groups (IG—intervention group and CG—control group) in an inpatientneurological rehabilitation unit. Both groups were assessed on admission and after six weeks ofrehabilitation. Rehabilitation was carried out six days a week for six weeks. The patients in theIG performed additional training using an EMG rehabilitation robot. Muscle fatigue was assessedusing an EMG rehabilitation robot; secondary outcomes were changes in hand grip strength, Fatigue
Assessment Scale, and functional assessment scales (Functional Independence Measure, Barthel
Index). Both groups improved in terms of the majority of measured parameters comparing pre- andpost-intervention results, except muscle fatigue. Muscle fatigue scores presented non-significant
improvement in the IG and non-significant deterioration in the CG. Using an EMG rehabilitation
robot in patients with PVF can be feasible and safe. To ascertain the effectiveness of such interventions,more studies are needed, particularly involving a larger sample and also assessing the participants’cognitive performance.
Keywords: SARS-CoV-2; exercises; physiotherapy; occupational therapy; hand grip strength

Tchounwou
Received: 30 June 2022
Accepted: 17 August 2022
Published: 20 August 2022
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1. Introduction
COVID-19 is a condition caused by a novel severe acute respiratory syndrome, which
has been known for the last two years. A SARS-CoV-2 infection manifests itself througha wide spectrum of symptoms, from asymptomatic to life-threatening and possibly, fatal [1–6]. The course of a SARS-CoV-2 infection is highly individual. Most patients recovercompletely; however, approximately 20% experience long-term adverse effects, including
fatigue (58%), headaches (44%), cognitive impairment (27%), excessive hair loss (25%),and dyspnoea (24%) [7]. Fatigue is one of the most common effects of COVID-19 [2] andcan still exist days after the first symptom of this condition [8,9]. According to the World
Health Organization International Classification of Diseases for Mortality and Morbidity
Statistics, 11th Revision (IDC-11), Post-Viral Fatigue (PVF) is a neurological condition; thedefinition of PVF includes chronic fatigue syndrome and myalgic encephalomyelitis [10].
With similar symptoms characterizing both chronic fatigue syndrome and amyalgic encephalomyelitis, and with no clear knowledge about their pathogenesis and no consensus
in terms of medical treatment, grouping these conditions under the definition of PVF helps

Int. J. Environ. Res. Public Health 2022, 19, 10398. https://doi.org/10.3390/ijerph191610398

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to facilitate research in this matter [11]. The main symptoms of PVF are chronic fatigue,
variable nonspecific myalgia, depression, and sleep disturbances [12].
PVF had previously been reported after the onset of other epidemics. In 2003, there wasan outbreak of the SARS-CoV-1 virus, which caused the SARS epidemic. Tansey et al. [13]assessed the health outcomes of recovered patients after hospital discharge and found that
more than half of their patients had experienced fatigue syndrome throughout their recovery: 64% reported fatigue at three months, 54% at six months, and 60% about twelve months
after discharge [13]. Chronic fatigue was also often observed after other viral infections suchas Epstein-Barr virus, cytomegalovirus, coxsackieviruses, and other coronaviruses [11].
Some COVID-19 patients subsequently develop PVF [14,15]. During the current pandemic, the most common symptom of post-viral sequelae was fatigue [7]. Kashif et al. [16]observed fatigue in recovered COVID-19 patients three months after hospital discharge.
PVF was also present in patients with a mild course of the disease [16,17]. It is hypothesizedthat PVF in Post-COVID patients results from building up cytokines, which affects thecentral nervous system’s lymphatic drainage—the “glymphatic system” [18].
The symptoms that patients reported after COVID-19 were not limited to those typicalof PVF. The most common Post-COVID complications were those related to patients’physical and cognitive functioning. Very often, those symptoms were dominated byneuromuscular and immune exhaustion such as a lack of strength (as a polyneuropathy),
disorders of neurological functions (such as cognitive disorders), aches and pains, problems
with sleeping but also those with moving and receiving sensory stimuli, disorders of theimmune system, and functions of the urogenital and digestive systems [19–21].
To cover the symptoms of patients after recovery from COVID-19, the synonymous
terms “Long-COVID” and “Post-COVID Syndrome” are used. There is no clear, unanimousdefinition of Long-COVID [22]. According to Raveendran et al., “Long-COVID” is thepresence of various symptoms, even weeks or months after the SARS-CoV-2 infection, andthe viral status is not related to those symptoms [23]. These authors proposed the term“Long-COVID” for both post-acute COVID-19 patients (from 3 weeks up to 3 months after
infection) and chronic ones (beyond 3 months after infection). The National Institute for
Health and Care Excellence (NICE) guidelines proposed the definitions of “Acute COVID”(when the signs and symptoms of COVID-19 are present for up to 4 weeks), “Ongoing
COVID” (when the signs and symptoms of COVID-19 are present from 4 weeks up to
12 weeks), and “Post-COVID Syndrome” (when the symptoms last more than 12 weeks
and cannot be explained by other underlying diseases). Post-COVID Syndrome, according
to these guidelines, can also be identified before the threshold of 12 weeks when a possibility of other underlying diseases is excluded. The NICE guidelines also introduced
the definition of “Long-COVID,” which is a term that includes both Ongoing COVID and
Post-COVID Syndrome.
Due to a lack of unified definitions, PVF and Long-COVID pose a challenge to modern
medicine. There is a growing need for research and the development of efficient therapeutic
options for patients. Post-COVID rehabilitation is usually comprised of light aerobic andbreathing exercises. Still, patients’ needs may vary from case to case. Notably, patients withmyalgic encephalomyelitis or chronic fatigue syndrome may present adverse responses toexercise. These patients may thus need a different approach [22]. The present pandemic
showed that, especially in the wake of a shortage of medical staff, robotic devices could
constitute a good option for the support and rehabilitation of COVID-19 patients [24,25].
Therefore, the aim of this paper was to assess the safety and feasibility of the use of an
EMG-Rehabilitation Robot in exercises performed by Post-COVID patients with PVF.
2. Materials and Methods
2.1. Study Design
A pilot prospective clinical study was conducted in the Neuro-rehabilitation Ward of
the Provincial Hospital in Poznan, Poland, between January and November 2021.

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3 of 112.2. Study GroupIn our study, we included 30 adults admitted to the inpatient neurological rehabilitation unit. The sample size was decided based on previous pilot studies concerning
biofeedback and/or robotic interventions [26–28]. Each participant was recruited withinformed consent to participate in the study.
2.3. Study Criteria
We included participants, all of whom were transferred directly from Intensive Care
Units (ICU) due to PVF after experiencing a severe course of COVID-19, according to
the criteria presented by Carod-Artal [29]. The exclusion criteria comprised additional
diagnosis of another active infection process, neoplastic, rheumatic, metabolic, endocrine,
autoimmune, and cardiovascular diseases, as well as conditions for which constant fatigue
is typical: multiple sclerosis, systemic lupus or Hashimoto’s disease, common hypothyroidism, Lyme disease, AIDS, diabetes, or myasthenia gravis, psychiatric disorders except
for unipolar depressed moods.
The study participants were randomly assigned to two groups (IG—intervention
group and CG—control group), thus constituting a 1:1 randomized study (allocation basedon the selection of the hospital computer). The total stay of each patient in the Rehabilitation
Unit was six weeks. Both groups were assessed twice: on admission and after six weeks.
2.4. Outcome Measure
The main outcome measure was muscle fatigue during the two types of rehabilitation
treatment. Additionally, the functional changes after rehabilitation were analyzed after two
types of neuro-rehabilitation.
2.5. Measurement
Sociodemographic data were collected from all subjects; additionally, the following
functional assessment tools were used: Functional Independence Measure (FIM), Barthel
Index (BI), handgrip strength (HGS), Fatigue Assessment Scale (FAS), and muscle fatigue
assessment model using the LUNA Rehabilitation Robot (EGZOTech, Poland, registration
number/TNP/MDD 0373/4038/2021).
The Functional Independence Measure (FIM) is used to assess the patient’s level ofdisability and a change in their status in response to rehabilitation or medical intervention.
FIM is an 18-item instrument that comprises measures of independence for self-care,including transfers, locomotion, communication, sphincter control, and cognition [30].
Each item is scored on a 7-point ordinal scale (ranging from 1 to 7). The higher thescore, the more independent the patient is in performing the task associated with that
item. In addition, the score reflects the level of assistance an individual needs—from total
independence to total assistance [31].
The Barthel Index is an ordinal scale used to measure performance in activities ofdaily living (ADL). BI measures the degree of assistance required by an individual on ten
items of mobility and self-care activities: feeding, personal toileting, bathing, dressing
and undressing, moving on and off a toilet, controlling the bladder, controlling bowel,
moving from wheelchair to bed and returning, walking on a level surface and ascending
and descending stairs. The Index is a three-item ordinal rating scale, and each item israted in terms of whether the patient can perform the task independently-2 points, withsome assistance-1 point, or is dependent on help 0 points. The maximum final score is 20(assistance in ADL is not needed) [32].
Handgrip strength was measured using a JAMAR Hand Dynamometer (Sammons
Preston Rolyan, USA). The measurement was performed in a sitting position in an armlesschair, with the forearm in a 90-degree flexed position. The average of three measurements(in kilograms) was recorded [33].
Fatigue Assessment Scale (FAS) is a 10-item fatigue questionnaire to assess fatigue inthe general population. Five questions reflect physical fatigue, and another five (questions 3

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and 6–9)—mental fatigue. The total score ranges from 10 to 50. A total FAS score of<22 indicates no fatigue, and a score of ≥22 indicates the presence of fatigue [34].
Muscle fatigue data were also retrieved from the EMG-rehabilitation robot regarding
the work of the muscles of the two-headed arm by means of the robot’s muscle fatigue
assessment model (isometric contraction tension). The test of EMG-based fatigue was
performed on the Biceps Brachii muscle by surface electrodes. The electrodes were placedon the line between the medial acromion and the fossa cubit at 1/3 from the fossa cubit
according to SENIAM [35]. The sampling frequency of the EMG signal was 1000 Hz. The
resulting data were filtered using a bandpass filter and an optional notch filter (50 Hz)
to prevent power line interferences. The upper limb extension was set and locked at
90 degrees of elbow flexion. The protocol of the test included 30 s of relaxation, then
30 s of contraction, and 30 s of relaxation. If the patient was not able to perform 30 s ofcontraction, the algorithm calculated the time when the muscle was in contraction basedon the EMG signal only. Based on the raw data exported from the robot, frequency analysis
was performed using external Python-based software, yielding isokinetic force and EMG(real-time activity) surface signal analysis.
2.6. Rehabilitation
Neurological rehabilitation in both study groups was carried out six days a week forsix weeks using neuromuscular re-education techniques (such as PNF—Proprioceptive
Neuromuscular Facilitation and Bobath therapy), exercises for movement coordination andbalance, with combined progressive endurance training. After the participants achieved
the ability to maintain a standing position and conduct active and resistance exercises,
progressive endurance training was performed from 35 percent of max HR (heart rate) to
70 percent of max HR (cycling and walking) according to the equation max HR = 220 − age
30 min per day. PNF or Bobath therapy was conducted 45 min per day. The patients in theIG had this training for 75 min per day, and the CG had this form of therapy for 120 min
a day.
Each patient (in both IG and CG) worked individually with a psychologist and speechtherapist five times a week for 30 min. Additionally, occupational therapy was performed
daily for a minimum of 30 min.
The patients in the IG, according to the intervention model of rehabilitation, performed additional training using an EMG rehabilitation robot—the EMG-rehabilitation
robot (two sessions of a total of 45 min a day for six days a week). The EMG-rehabilitation
robot enables the isokinetic-isotonic training of selected muscle groups using reactive electromyography (EMG-triggered), providing assisting robot movement, which is activated by
the patient’s muscle bioelectric activity signal measured in real-time with surface electrodes.
During exercises with the EMG robot, the participants conducted flexion and extension of
the elbow and flexion, extension, and abduction in the glenohumeral joint. The muscles of
the upper limb (such as biceps brachii, triceps brachii, and deltoid muscle) were used totrigger the movement. Figure 1 presents the setup of the robotic device during one of theexercises. All of the EMG procedures were carried out in accordance with the SENIAM
guidelines [35].
Luna EMG is a rehabilitation robot specifically designed to support the physiotherapyof neurological patients with muscle weakness. It is an all-in-one platform for complex
personalized therapy for patients suffering from neurological conditions. It tackles clinicalissues such as muscle weakness, mobility disorders, gait problems, and range of motion
restrictions, specifically by automating the process of personalized, motivating physiotherapy based on electromyography combined with force and position sensing. The robot
was attached to the patient through extensions–interchangeable mechanical parts that areconnected to the patient by straps or by the grip. The movements are controlled by aWindows application from a therapist panel, which provides a User Interface, patient management, reporting module, and Internet connectivity for the purpose of remote diagnostics
and oversight.

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A total of 30 participants (15 in IG and 15 in CG) were included (Figure 2 presentstheflowdiagramof the study). Data from two participants (one from each group) regard3. Statistical
Analysising muscle fatigue were not available; therefore, the analysis of this particular outcome
Statistical analysis was performed with the Statistica 13.3 software (TIBCO Software,comprised data from 28 participants.
Warszawa, Poland). The normality of the distribution was checked with the Shapiro-Wilk
test. In the absence of a normal distribution and for ordinal variables, the Mann–Whitney
test was used to compare the results in two groups. The Wilcoxon signed-rank test wasused for intragroup comparisons. Data are presented as medians and range. The χ2 test

significant.
4. Results
A total of 30 participants (15 in IG and 15 in CG) were included (Figure 2 presents
the flow diagram of the study). Data from two participants (one from each group) regardInt. J. Environ. Res. Public Health 2022, 19, 10398
6 of 11
ing muscle fatigue were not available; therefore, the analysis of this particular outcome
comprised data from 28 participants.

Figure 2. Study participants flowchart.
Figure 2. Study participants flowchart.

Nostatisticallystatisticallysignificant
differences
were
found
betweencharacteristics
Nosignificant
differences
were
found
betweengroupgroupcharacteristics(con(concerning
age,
sex,
weight,height,
BMI,
time
in
the
ICU,
and
time
intubated)baselinecerning age, sex, weight, height, BMI, time in the ICU, and time intubated) atatbaseline(Table1).1).
(TableTable 1. Baseline group characteristics.

Sex (%) *FemaleMale
Age (years) +Level of education % *primary

Intervention Group

Control Group40
60
69 (43–81)

26.67
13.33
66 (39–75)

6.67

0

p-Value
0.439

0.372
0.659

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Table 1. Cont.

vocational
secondaryhigh
Height (cm) +Weight (kg) +BMI +Time in the ICU (days)
Time intubated (days)

Intervention Group

Control Group

p-Value

26.67
46.67
20
171 (150–188)
77 (50–103)
26.33 (20.81–32.32)
25 (20–41)
23(15–35)

40
46.67
13.33
173 (154–192)
80 (49–97)
25.25 (19.14–36.07)
24 (20–44)
21(15–33)

0.442
0.561
0.130
0.917
0.604

*—data presented as percentage of overall group population; +—data presented data presented as median andrange; ICU—intensive care unit.

Both the IG and CG improved in terms of the majority of measured parameterscomparing the pre- and post-intervention results, except for muscle fatigue measured
by EMG. Muscle fatigue scores presented non-significant improvement in IG and nonsignificant deterioration in CG. The results of the pre-post comparison are presented inTable 2.
Table 2. Comparison of the pre- and post-intervention results.
Intervention Group

Control Group

Outcome Measure(Median and Range)

Pre-Intervention

Post-Interventionp

Pre-Intervention

Post-Intervention

p

FIM
HGS
BI
FAS
Fatigue (EMG)

85 (8–120)
18 (0–35)
11 (2–14)
25 (15–42)−5.95 (−29.2–5.4)

117 (78–136)
20 (1–37)
18 (15–20)
23 (7–38)−6.8 (−17.6–20.9)

0.001
0.001
0.001
0.001
0.778

89 (32–120)
20 (10–39)
12 (3–14)
26 (14–42)−2.2 (−20.1–43.6)

117 (5–126)
22 (14–40)
19 (2–20)
26 (13–48)−1.05 (−22.2–10.2)

0.005
0.007
0.001
0.041
0.975

FIM—Functional Independence Measure, HGS—Handgrip strength, BI—Barthel Index, FAS—Fatigue Assessment
Scale, Fatigue (EMG)—muscle fatigue calculated from EMG measurement data, expressed as a percentage of theslope of the frequency curve.

The comparison of mean changes of measured parameters did not reveal any statistically significant differences between the study groups. The results are presented inTable 3.
Table 3. Comparison of mean pre-post changes of outcomes between groups.
Outcome Measure(Median and Range)

Intervention Group

Control Group

p

FIM
HGS
BI
FAS
Fatigue (EMG)

26 (16–113)
3 (0–10)8 (4–14)−2 (−11–0)
0 (−14.9–34.7)

23 (−27–54)
4 (−9–10)
6 (−3–11)−2 (−7–7)
2.8 (−55.4–11.3)

0.137
0.367
0.233
0.412
0.909

FIM—Functional Independence Measure, HGS—Handgrip strength, BI—Barthel Index, FAS—Fatigue Assessment
Scale, Fatigue (EMG)—muscle fatigue calculated from EMG measurement data, expressed as a percentage of theslope of the frequency curve.

The anonymized EMG charts from pre- and post-intervention measurements of allof the participants are included in the supplementary material. No adverse effects ofthe exercises on EMG—rehabilitation robot (such as musculoskeletal pain, numbness, ordeterioration of physical function) were reported by the participants. All of the participants
were able to perform assigned EMG—rehabilitation robot exercises daily for a full 45 min.
5. Discussion
In this pilot study, the feasibility of the concept of using the EMG—rehabilitation robot
in exercises for patients with PVF syndrome was verified positively. The study participants

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8 of 11reported no adverse outcomes, and no dropouts from the study were observed. The lackof data from two participants resulted from transferring them to another facility. It must
be noted that not only no adverse outcomes were reported but also improvement in bothgroups was observed. Based on that, conducting further research on this topic following themethodology presented in this study can be safe for patients and is not likely to negatively
impact their outcomes of rehabilitation.
The use of robots in rehabilitation started on a larger scale in 1990 [36], and this branchof medicine and technology is still developing. Robotic devices have many advantages,
among them the ability to maintain precise continuous movement and the potential for
actively engaging users [36]. These features make them valuable in the rehabilitation
process and deliver the base for efforts to implement the robots in a variety of conditions,with stroke leading among them. Villafañe et al. [37] conducted a study of the efficacy
of robot-assisted rehabilitation in stroke patients with hand paralysis. The experimentalgroup received passive mobilization by a robotic device in addition to their regular physicaltherapy and occupational therapy, while the control group performed more physicaland occupational therapy instead. The authors reported greater improvement in theexperimental group and concluded that robotic rehabilitation could be beneficial in regard
to pain and spasticity. Bustamante-Valles et al. [38] performed a study comparing traditional
therapy after stroke and robot-assisted circuit training, concluding that robot-assisted
therapy can be as efficient as traditional occupational or physical therapy. The authors also
showed that the cost of 2 h of conventional therapy to the national healthcare system in
Mexico was 19.21 USD, while 2 h of robot-assisted therapy cost only 6.99 USD. The authors’conclusion was that involving robots in the rehabilitation process is not only substantively
effective but also cost-effective compared to conventional rehabilitation or occupational
therapy. In 2017, a systematic review, including ten trials involving 502 participants,
showed that robot-assisted gait training improved gait parameters, participants’ mobility,
and independence significantly greater than conventional gait training in patients withincomplete spinal cord injury [39].
As Yong [22] stated, among all of the available therapeutic options, only rehabilitation
seems to be effective in the treatment of symptoms of Long-COVID. Many studies focus
on Post-COVID symptoms or the need for rehabilitation [37–40] or propose guidelines
and rehabilitation programs [40–42], but there is a distinct lack of studies assessing rehabilitation effectiveness in patients with PVF [12,43]. Udina et al. [44] conducted a study
on the effect of Post-COVID rehabilitation on COVID-19 survivors’ independence andphysical performance. In their study, during the rehabilitation, the patients were obliged toperform 30 min of exercises daily (involving resistance, endurance, balance, and breathing
exercises). The authors demonstrated that exercises improved participants’ independence
and physical performance. Spielmanns et al. [43] also assessed the effect of Post-COVID
rehabilitation on patients’ physical performance and independence. The authors reported
improvement in regard to FIM scores after rehabilitation in both the Post-COVID group(from 100 (±15.1) in pre- to 111 (±15.0) in post-rehabilitation measurements) and thecontrol group, which was comprised of patients with lung diseases–from 99.7 (±9.72) to107 (±10.7). These results are consistent with ours regarding the functional independence
indices. Nopp et al. [45] measured fatigue by FAS, similarly to the present study. The
authors observed improvement in terms of FAS scores and 6-min walk test after six weeks
of individualized pulmonary rehabilitation. Their protocol of rehabilitation contained
endurance, strength, and respiratory muscle training alongside psychologic, dietary, andnutritional interventions. Daynes et al. [46] investigated post-COVID-19 rehabilitation
efficiency in improving fatigue symptoms, breathlessness, exercise capacity, and cognition.
Thirty individuals attended twelve rehabilitation sessions each (two per week). Each session consisted of physical rehabilitation (endurance, aerobic, and strengthening exercises)and patients’ education, which covered various topics linked with pulmonary functioning,such as fatigue, cough, breathlessness, smell, and other conditions related to COVID-19
symptoms (i.e., loss of taste, cognition problems, and anxiety). The participants showedInt. J. Environ. Res. Public Health 2022, 19, 10398

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improvement in regard to fatigue. Muscle fatigue was measured in none of the abovementioned studies [43–46]. To the best of our knowledge, the present study is the first one
tackling the topic of using a robotic device in the PVF syndrome. Therefore, comparing our
results in this particular domain to the literature is difficult.
Our study demonstrated that implementing an EMG robot-rehabilitation protocolin PVF patients is feasible and safe. Similar results were obtained by different authors instudies using comparable devices. Kim et al. [47] conducted a study on 12 stroke survivors
who were exercising on an electromyography-triggered hand robot. The authors observed
an improvement concerning hand function indices with no serious adverse effects. Stroke
patients were also the target of a feasibility study conducted by Singer et al. [48]. The
authors observed that EMG- triggered electrostimulation was feasible, safe, and efficient in
improving stroke patients’ hand function.
It should be noted that our study has some limitations. First of all, the lack of significant
difference in terms of improvement between the IG and CG could result from the toosmall sample size. As the present study aimed to assess the feasibility and safety of thepresented protocol, further research involving larger samples is needed to establish theactual influence of exercises on a rehabilitation robot on muscle fatigue and fatigue-related
outcomes. A further limitation is that PVF is often accompanied by cognitive disorders, andexercises on the EMG—rehabilitation robot required notable focus and psychological effort
to perform the exercises and measurements correctly. Further studies are needed to analyze
if exercising on an EMG—rehabilitation robot affects cognitive abilities. The third limitation
results from the fact that this study is, to the best of our knowledge, the first one assessing
an EMG-driven device in the therapy of PVF. While it can be perceived as a strength of
the study, it must be pointed out that there exists no literature with which to relate ourresults. While most of the studies assessing the effectiveness of such devices target stroke
patients, due to the different nature and causes of PVF and stroke-related impairments,
the effectiveness of such devices can differ between those groups. The different exercise
regimens in the studied groups can also be viewed as a limitation.
6. Conclusions
According to the results obtained in this feasibility study, using an EMG-robot in the
rehabilitation of patients with PVF can be feasible and safe. To ascertain the effectiveness ofsuch interventions in this group of patients, more studies are needed, particularly involving
a larger sample and also assessing the participants’ cognitive performance.
Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph191610398/s1, Supplementary materials contain anonymized
EMG charts from pre- and post-intervention measurements of all participants (Intervention Group,
pre- and post intervention; Control Group, pre- and post intervention).
Author Contributions: Conceptualization, E.Z., S.T. and K.H.; Formal analysis, S.T., A.R. and A.P.;
Investigation, R.M.; Methodology, E.Z., T.T. and D.K.; Project administration, R.M.; Resources, K.H.;
Supervision, K.H.; Writing—original draft, E.Z. and T.T.; Writing—review & editing, E.Z., S.T., T.T.,
D.K., A.P. and K.H. All authors have read and agreed to the published version of the manuscript.
Funding: The APC of this paper was covered from a National Center for Research and Developmentof Poland grant no. POIR.01.01.01-00-2077/15.
Institutional Review Board Statement: The approval for this research was issued by the Bioethics
Committee of Poznan University of Medical Sciences (Protocol No. 514/21). The study has beenregistered with ClinicalTrials.gov, ID: NCT05130736.
Informed Consent Statement: Informed consent statement was obtained from all participants.
Data Availability Statement: The authors confirm that the data supporting the findings of this study
are available within the article, its’ Supplementary Materials and are available from the corresponding
author, [T.T.], upon reasonable request.

Int. J. Environ. Res. Public Health 2022, 19, 10398

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Acknowledgments: The authors wish to thank the EGZOTech company for providing the Luna EMG
robot free of charge, and particularly Michal Mikulski for software development for the study.
Conflicts of Interest: D.K. (clinical specialist in physiotherapy) and A.R. (biomedical engineer) are
employees of the EGZOTech company, which designed the Luna EMG robot.

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