sensors
ArticleThe Effect of Using a Rehabilitation Robot for Patients with
Post-Coronavirus Disease (COVID-19) Fatigue Syndrome
Tomasz Trzmiel 1, * , Renata Marchewka 2 , Anna Pieczyńska 1 , Ewa Zasadzka 1 , Igor Zubrycki 3 ,Dominika Kozak 4,5 , Michał Mikulski 4 , Anna Poświata 4 , Sławomir Tobis 1 and Katarzyna Hojan 1,2,6
1

2
3
4

5
6

*

Citation: Trzmiel, T.; Marchewka, R.;
Pieczyńska, A.; Zasadzka, E.;
Zubrycki, I.; Kozak, D.; Mikulski, M.;

Department of Occupational Therapy, Poznan University of Medical Sciences, 60-781 Poznan, Poland;
stobis@ump.edu.pl (S.T.); khojan@ump.edu.pl (K.H.)
Neurorehabilitation Ward, Greater Poland Provincial Hospital, 60-480 Poznan, Poland
Institute of Automatic Control, Lodz University of Technology, 90-537 Łódź, Poland; igor.zubrycki@p.lodz.pl
Egzotech sp. z o.o., 44-100 Gliwice, Poland; dominika.kozak@egzotech.com (D.K.);
michael.mikulski@egzotech.com (M.M.); anna.poswiata@egzotech.com (A.P.)
Department of Physiotherapy, University of Health Science, 85-067 Bydgoszcz, Poland
Department of Rehabilitation, Greater Poland Cancer Centre, 61-866 Poznan, Poland
Correspondence: ttrzmiel@ump.edu.pl; Tel.: +48-618546573

Abstract: The aim of this study was to compare the effectiveness of traditional neurological rehabilitation and neurological rehabilitation combined with a rehabilitation robot for patients with
post-COVID-19 fatigue syndrome. Eighty-six participants transferred from intensive care units due
to post-viral fatigue after COVID-19 were randomly divided into two groups: the intervention groupand the control group. The control group received standard neurological rehabilitation for 120 min aday, while the intervention group received the same neurological rehabilitation for 75 min a day, complemented by 45 min of exercises on the rehabilitation robot. The Berg scale, Tinetti scale, six-minutewalking test, isokinetic muscle force test, hand grip strength, Barthel Index, and Functional Independence Measure were used to measure the outcomes. Both groups improved similarly during therehabilitation. Between groups, a comparison of before/after changes revealed that the intervention
group improved better in terms of Functional Independence Measure (p = 0.015) and mean extensor
strength (p = 0.023). The use of EMG-driven robots in the rehabilitation of post-COVID-19 fatigue
syndrome patients was shown to be effective.

Poświata, A.; Tobis, S.; Hojan, K. The
Effect of Using a Rehabilitation Robot
for Patients with Post-Coronavirus

Keywords: SARS-CoV-2; exercises; hand grip strength; occupational therapy; physiotherapy

Disease (COVID-19) Fatigue
Syndrome. Sensors 2023, 23, 8120.
https://doi.org/10.3390/s23198120
Academic Editor: Iulian I.
Iordachita
Received: 13 August 2023
Revised: 23 September 2023
Accepted: 25 September 2023
Published: 27 September 2023

Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
In recent years, a new global health problem emerged due to the novel coronavirus—
SARS-CoV-2—causing coronavirus disease 2019 (COVID-19). This virus, which first appeared in China, has quickly spread worldwide, causing a global pandemic. In addition to
the characteristic symptoms of respiratory tract infections, such as fever, cough, or fatigue,
in the course of COVID-19, many other symptoms are likely to appear, including loss oftaste and smell, sore throat, headache, muscle or joint pain, skin rash, nausea and vomiting,and dizziness [1]. In the vast majority of cases, the course of the disease is mild (80%),
according to the WHO. However, some patients require hospitalization, and the condition
might even lead to death. Importantly, not only the course of COVID-19 constituted apublic health problem [2]. It is estimated that up to 20% of patients who had recovered
suffered from long-term complications after contracting COVID-19 [3,4], which was also
observed in patients with a mild course of the disease [5].
A similar phenomenon has also been reported for other viral diseases. Tansey et al. [6]showed that in the case of the SARS-CoV-1 virus epidemic, which took place in 2003, overhalf of the patients discharged from hospitals after treatment of the disease developed
fatigue syndrome. Among the respondents, as many as 64% experienced fatigue after

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2 of 13three months, and 60% even a year after discharge. Similar symptoms were also observed
not only after other coronavirus infections but also for viruses such as Epstein–Barr virus,
coxsackieviruses, and cytomegaloviruses [7]. Currently, there is no consensus on a termor definition in the scientific literature to describe a patient’s symptoms persisting after
recovering from COVID-19. The most commonly used terms are “Long-COVID” and“Post-COVID Syndrome”; however, the definitions of these terms also vary. Raveendran
et al. [8] use the term “Long-COVID” to describe both post-acute COVID-19 (the state
between three weeks and three months from onset) and chronic COVID-19 (over threemonths from onset of the disease). The National Institute for Health and Care Excellence(NICE) guidelines proposed other definitions. “Acute COVID” is defined as a state where
symptoms and signs are present for up to 4 weeks. “Post-COVID syndrome” is defined asa state where the symptoms last more than three months. According to these definitions,
between “Acute-COVID” and “Post-COVID syndrome”, there is “Ongoing COVID”. The
NICE guidelines define “Long-COVID” as a term that includes ongoing COVID and postCOVID syndrome [9]. Despite the lack of agreement on definitions in the literature, there isa consensus opinion that persistent symptoms after COVID-19 infection pose a challenge
to modern medicine, particularly those related to fatigue and reduced ability to perform
physical activity (or even activities of daily living).
Fatigue syndrome observed after COVID-19 is characterized by chronic fatigue, sleep
problems, depressive symptoms, and myalgia [10]. The underlying pathomechanisms ofsuch conditions remain unclear. Some theories point out that they may be caused by postviral immune system alterations. However, there are still no sufficient data to support thisstatement [11]. Another hypothesis is that the infection alters the drainage of cerebrospinal
fluid and lymph from the brain, which causes an accumulation of cerebrospinal fluid, leading to a form of intracranial hypertension [10]. This statement may be partially supportedby the fact that patients with chronic fatigue syndrome who underwent cerebrospinal
fluid drainage improved. Other authors suggest that post-COVID symptoms, including
fatigue, may be related to the disfunction of both pulmonary cells and respiratory muscles
as the SARS-CoV-2 virus uses the angiotensin-converting enzyme 2 to enter both kinds ofcells. Persistent symptoms may be explained by changes in respiratory muscle function as
patients improve after rehabilitation and manual therapy of the diaphragm [12]. Despite
different hypotheses and ongoing research, there is no clear evidence on the identity of thecause of the observed fatigue syndrome; its etiology is probably multifactorial. There is aneed for efficient ways to manage post-COVID fatigue and loss of independence. Currently,
rehabilitation is considered one of the most effective therapeutic options [9].
Various rehabilitation approaches are used to manage post-COVID symptoms [13–15].
Some authors suggested pulmonary rehabilitation [14], while others suggested combined
aerobic and resistance training [13]. In every case, rehabilitation should be individually
tailored in regard to the patient’s abilities, state of health, fitness, and symptoms [9,15].
Patients’ ability to perform exercises may be altered differently in each person, so it isimportant to perform a proper assessment of a patient’s physical state and adjust exercises,
their length, load, and frequency individually. It is also important to remember that aside
from symptoms related to COVID-19, patients may also have other conditions, whichfurther strengthens the need for an individual approach during the rehabilitation process.
Due to the growing workload of medical staff as well as the ever-increasing costs ofrehabilitation, new methods of patient therapy are also sought, involving advanced medical
devices that have the potential to reduce the burden on multidisciplinary rehabilitation
teams. In light of the abovementioned problems, robotic devices could greatly improve theability of healthcare to manage post-COVID patients and their symptoms [4,16]. Therefore,
the aim of this study was to compare the effectiveness of traditional neurological rehabilitation against neurological rehabilitation combined with a rehabilitation robot for patientswith post-COVID-19 fatigue syndrome.

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2. Materials and Methods
A prospective randomized clinical trial was conducted in the Neuro-rehabilitation
Ward of the Provincial Hospital in Poznan, Poland, between January and August 2022. The
study has been registered with ClinicalTrials.gov, ID: NCT05130736.
Participants who were transferred directly from Intensive Care Units (ICUs) due topost-viral fatigue after experiencing a severe course of COVID-19 according to the criteriapresented by Carod-Artal [17] were included. Among those, there were four main criteria,
namely deterioration of the ability to perform tasks carried out before the disease, severe
fatigue not improved by rest, discomfort after physical activity, and nonrestorative sleep,
and two additional ones: cognitive impairment and orthostatic intolerance. At least threemain criteria and one additional criterion had to be met for a patient to be included in the
study. As patients were transferred straight from the ICU, we waived the condition that theabove symptoms had to be present for more than six months. Patients were excluded if theypresented any of the following: active medical condition (infections; tumors; rheumatological, metabolic, endocrine, autoimmunological, cardiovascular diseases), bipolar disorder,
dementia, nutritional disorders, addiction to alcohol or psychoactive substances, severe
obesity, overtraining. A total of 86 participants were recruited. Participants were randomly
divided into two groups: the intervention group (IG) and the control group (CG).
The CG received standard neurological rehabilitation for 120 min a day, while theIG received the same neurological rehabilitation for 75 min a day, complemented by
45 min of exercises on the rehabilitation robot (Egzotech sp. z o.o., Gliwice, Poland). The
rehabilitation robot was a device constructed to support physiotherapists and patientsduring the rehabilitation process. Due to the fact that the robot is EMG-driven, the device
is able to help patients with relatively low muscle strength perform active exercises. Even if
muscle contraction, due to its weakness, is not visible, electrodes applied above the muscles
record changes in their activity during the contraction. Those signals trigger the actuator,
which moves the movable parts (extensions) of the device. A limb of the patient is attachedto those extensions by straps, which allows them to perform a movement (such as flexionand extension of the elbow) and control the movement even in a situation when normal
movement will not be possible due to muscle weakness. The intervention was described indetail in a previous feasibility study [4].
2.1. Measurement
Several measurement tools were used to assess functional physical ability (Berg scale,
Tinetti scale, six-minute walking test (6MWT)), muscle strength (isokinetic muscle force ofelbow flexors and extensors, hand grip strength (GS)), and independence in activities ofdaily living (Barthel Index, Functional Independence Measure).
The Berg scale is used to assess balance and measures the static and dynamic balanceusing 14 motor tasks. Each task is scored from 0 to 4, where 0 means that the subject is
not able to perform the task, and 4 is scored when the subject performed the task in thebest way assigned to the task. The maximum total score is 56 points, and it indicates thatthe patient is fully independent in ambulation [18]. The Tinetti scale assesses balance instanding and sitting, and its results predict the risk of falls. It comprises 9 tasks. First,
balance during sitting is assessed. Then, the patient must stand and maintain balance inthree different positions. The ability to maintain balance is assessed in each position [19].
The 6MWT is used to measure physical exercise tolerance. It should be performed indoors,
on a flat surface with a marked distance of 30 m. Patients are asked to walk the marked
distance as many times as they are able for 6 min [20].
The isokinetic muscle force test of elbow flexors and extensors in the dominant hand
was performed on the rehabilitation robot. Patients were seated in a chair (dominant side
toward the robot) with a straight back, hips and knees in 90 degrees of flexion, and feeton the floor. The dominant forearm was fixed by two straps and with the hand gripping ahandle. The axis of the elbow was aligned with the axis of the dynamometer (Figure 1).

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Muscle force was assessed at 90 degrees of flexion in the elbow. Participants performed
maximal voluntary concentric flexion and extension separated by a break of 1 min.

Figure 1. Participant upper limb position during elbow flexion/extension strength test (source–
authors’ own).

The HGS test was performed for the dominant hand using the JAMAR hydraulic hand
dynamometer (Sammons Preston Rolyan, Bolingbrook, IL, USA). The test was performedin accordance with the gold standard of the American Society of Hand Therapists [21].
Participants sat straight on a chair without armrests, with knees and hips in 90-degree
flexion and feet on the ground. The elbow was flexed to 90 degrees; the wrist was straight inintermediate rotation (thumb points to the ceiling). During the measurement, subjects wereencouraged to squeeze the handle of the dynamometer as hard as possible. Contraction
was maintained for 5 s, and three measures of dominant hand strength were conducted,
with a 1 min break between each measure. The best result from the three measurements
was taken for analysis.
The Barthel Index is a scale used to assess independence and the need for assistancein performing activities related to toileting, sphincter control, transferring to and from awheelchair, and ambulation [22]. Functional independence was also assessed using the
FIM. The FIM is an 18-item instrument that includes measures of independence in self-care,including ambulation, locomotion, communication, sphincter control, and cognition [23].
Each task of the FIM is scored from 1 point (which indicates that the subject is entirely
dependent on assistance) to 7 points (which indicates that the subject is fully independent
in performing the evaluated task).
2.2. Statistical Analysis
Statistical analysis was performed with Statistica 13.3 software (TIBCO Software/StatSoft
Polska Sp. z o.o., Krakow, Poland). The threshold for statistical significance was set at p < 0.05.
The Shapiro–Wilk test was used to check distribution normality. Student’s t-test was used
to initially compare the results of the two groups. In the wake of the absence of a normal

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distribution, the Mann–Whitney U test was used. The ANOVA test for repeated measures wasused to examine differences over time, taking into account the qualitative variable—group.
The assumption of sphericity was checked with the Mauchley test; if sphericity was violated,
the Greenhaus–Geisser correction was applied. Tukey’s test was used for post hoc analyses.
3. Results
A total of 86 participants were included in the study. However, due to various causes,
five participants did not complete the study. Therefore, data from 81 participants (42 in IG
and 39 in CG) were analyzed. Figure 2 presents the flow diagram of the study.

Figure 2. Study participant flowchart.

There were significant differences between groups in terms of weight and BMI, yetgroups did not differ in age, height, or any of the measured parameters at the baseline.
Group characteristics comparison at the baseline is presented in Table 1.

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Table 1. Participants’ characteristics at baseline.

Age
HeightWeight
BMI
FIM
Berg
HGS
Tinetti
6MW
BarthelMean flexion
strengthPeak flexion
strength
Mean extension
strengthPeak extension
strength

Intervention Group n = 42
MeanSD

Control Group n = 39
MeanSD

p-Value

67.38
169.86
81.55
28.19
77.71
27.67
13.20
12.81
115.81
10.48

8.46
9.00
12.89
3.52
21.63
15.16
10.40
8.32
112.093.97

64.92
170.95
75.21
25.64
83.59
30.28
15.69
15.03
134.41
11.00

11.74
9.31
13.97
3.89
24.14
16.08
11.29
7.78
121.36
3.10

0.280
0.593
0.036
0.003
0.138
0.303
0.359
0.223
0.491
0.762

5.04

4.77

5.91

5.30

0.105

16.22

10.35

18.40

12.34

0.397

4.75

3.83

5.95

4.51

0.157

15.31

9.11

16.65

10.52

0.674

BMI—body mass index; FIM—Functional Independence Measure; Berg—Berg scale; HGS—hand grip strength;
Tinetti—Tinetti test; 6MWT—six-minute walking test; Barthel—Barthel Index; flexion strength—elbow flexion
torque; extension strength—elbow extension torque.

A comparison of the results before and after rehabilitation in both groups is presented
in Table 2. In both the intervention and control groups, all parameters improved significantly over time. A comparison of pre- and post-rehabilitation FIM results is presented in
Figure 3, and a comparison of its motor subscale is presented in Figure 4.
Table 2. Repeated measures ANOVA results for all outcome measures.
Intervention Groupn = 42Pre
Post
FIM
FIM self-care
FIM sphincter control
FIM transverse
FIM locomotion
FIM motor subscale
FIM communication
FIM social cognition
FIM cognition subscale
Berg
HGS
Tinetti
6MW
BarthelMean flexion strength
Peak flexion strength
Mean extension strength
Peak extension strength

79.76 ± 18.32
25.95 ± 8.68
9.48 ± 2.94
11.43 ± 4.59
5.45 ± 3.08
52.31 ± 17.07
11.57 ± 2.34
15.88 ± 3.15
27.45 ± 5.07
27.67 ± 15.16
13.20 ± 10.40
12.81 ± 8.32
115.81 ± 112.09
10.48 ± 3.97
5.04 ± 4.77
16.22 ± 10.35
4.75 ± 3.83
15.31 ± 9.11

110.60 ± 13.64
37.24 ± 5.53
12.71 ± 1.98
18.33 ± 3.27
10.74 ± 2.72
79.02 ± 12.30
13.10 ± 1.28
18.48 ± 2.23
31.57 ± 3.34
44.57 ± 12.83
18.08 ± 11.12
22.38 ± 5.89
240.02 ± 123.58
17.95 ± 2.38
7.42 ± 5.64
21.83 ± 11.52
7.36 ± 4.73
30.39 ± 26.08

Control Groupn = 39Pre
Post
84.13 ± 24.15
28.31 ± 10.55
10.03 ± 3.33
12.44 ± 5.83
6.26 ± 3.79
57.03 ± 20.83
11.33 ± 2.31
15.77 ± 3.84
27.10 ± 5.83
30.28 ± 16.08
15.69 ± 11.29
15.03 ± 7.78
134.41 ± 121.36
11.00 ± 3.10
5.91 ± 5.30
18.40 ± 12.34
5.95 ± 4.51
6.99 ± 4.91

108.62 ± 25.09
36.31 ± 9.00
12.38 ± 3.11
18.05 ± 4.57
10.95 ± 3.15
77.69 ± 19.22
12.87 ± 2.26
18.05 ± 4.39
30.92 ± 6.59
45.18 ± 14.25
20.22 ± 12.06
23.00 ± 5.91
256.69 ± 123.94
17.44 ± 4.23
6.97 ± 4.94
20.90 ± 11.16
16.65 ± 10.52
35.18 ± 29.92

Time

ANOVA
p-ValueGroup
Group * Time

<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
0.003<0.001<0.001<0.001

0.78
0.679
0.851
0.434
0.699
0.64
0.57
0.69
0.638
0.592
0.342
0.324
0.483
0.996
0.838
0.79
0.633
0.391

0.076
0.055
0.097
0.325
0.142
0.047
0.975
0.649
0.778
0.412
0.768
0.225
0.919
0.123
0.233
0.116
0.118
0.572

* Data are expressed as mean and standard deviation. BMI—body mass index; FIM—Functional Independence
Measure; Berg—Berg scale; HGS—hand grip strength; Tinetti—Tinetti test; 6MWT—six-minute walking test;
Barthel—Barthel Index; flexion strength—elbow flexion torque; extension strength—elbow extension torque.

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Figure 3. Comparison of baseline and post-rehabilitation FIM results.

Figure 4. Comparison of baseline and post-rehabilitation FIM motor subscale results.

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In the analysis of the results of repeated measures ANOVA, taking into account thedivision into groups, no intra-group interactions were found in any parameter except
the FIM motor subscale (p = 0.047). The post hoc test showed differences between the
FIM motor subscale before rehabilitation and the measurement after rehabilitation in bothgroups (<0.001).
4. Discussion
The use of robots in rehabilitation gained significant traction in 1990 [24], and thisdomain of medicine and technology continues to evolve. Robotic devices offer numerous
advantages, including the ability to maintain precise and continuous movements, as well as
the potential to engage users actively. These characteristics make them highly valuable in
the rehabilitation process, forming the basis for the use of robots in various conditions, withstroke rehabilitation being particularly prominent. The vast majority of studies concerning
rehabilitation robots focus on stroke patients; except for our previous pilot study, there are
no available analyses comparing the effectiveness of robotic devices in the rehabilitationof patients with post-COVID-19 fatigue syndrome or other fatigue syndromes. The aimof this study was to compare the effectiveness of traditional neurological rehabilitationand neurological rehabilitation combined with a rehabilitation robot for patients with
post-COVID-19-fatigue syndrome. We found that both groups improved similarly during
the rehabilitation despite implementing different rehabilitation protocols. FIM is a toolthat allows the measurement of independence in various domains. Some of the assessedtasks are not directly connected to upper limb function or physical function. In the present
study, both groups improved after rehabilitation in all domains measured by FIM. ANOVA
showed significant group × time interaction only in the summary score of the motor
subscale. This result can be expected since the rehabilitation concentrated on the motoric
aspect of patients’ functioning. However, the significance of this result is low (p = 0.047,
almost on the verge of being insignificant), and post hoc analysis showed no statisticalsignificance in the comparison of post-intervention measures between groups. Similarly, theBarthel scale, which measures a patient’s independence, showed significant improvements
in both groups with no significant group × time interactions. These results show that both
rehabilitation approaches lead to patients’ improvements in terms of independence, andneither approach is more or less beneficial than the other. The observed improvement in
the FIM and Barthel scale should be considered clinically important, as changes in thosescales, even by a few points, indicate meaningful changes in independence and can be
related to decreased need for care [25]. These findings are coherent with the findings ofother authors who compared traditional and robot-assisted rehabilitation [26–28].
Villafañe et al. [26] compared robot-assisted rehabilitation with traditional rehabilitation for patients with hand paralysis after stroke. Their study was conducted on thirty-two
patients with hand paralysis after stroke who participated in rehabilitation. The control
group received 90 min of traditional rehabilitation, while the study group received 60 min of
traditional rehabilitation and 30 min of hand mobilization performed with a robotic device.
Both groups improved, which led the authors to the conclusion that robotic interventions
in rehabilitation should be supported. The present study shows comparable results with asimilar methodology implemented. Supplementing part of traditional rehabilitation withrobotic intervention did not negatively alter the improvement of patients with post-COVID
fatigue syndrome. Mayr et al. [27] compared rehabilitation with gait training against rehabilitation with added robotic intervention to improve gait in patients after stroke. They
found no significant differences between the study and control groups, while both groups
improved from the baseline. Similar results were obtained in the present study. Rodgers
et al. [28] compared the effects of robot-assisted training, enhanced upper limb therapy, andtraditional rehabilitation on upper limb function after a stroke. Their study was conducted
on 770 participants, and the results showed that robot-assisted training and enhanced upper
limb therapy (as a substitute for traditional therapy) resulted in similar improvements. The
abovementioned authors also obtained results similar to the results shown by the presentSensors 2023, 23, 8120

9 of 13study. However, Rodgers et al. did not recommend using robot-assisted rehabilitation due
to the high cost of such interventions compared to usual therapy. The authors calculated
that robot-assisted rehabilitation was the most costly form of treatment in their setting(GBP 5387 per participant), while traditional therapy cost was the lowest (GBP 3785 perparticipant). However, Bustamante-Valles et al. [29] obtained the opposite results. Whilethey found that robot-assisted therapy and traditional rehabilitation after stroke wereequally effective (which is coherent with our findings), they calculated that robot-assisted
intervention was cheaper than traditional rehabilitation (6.99 vs. 19.21 USD/2 h, respectively). Unlike Rogers et al., Bustamante Valles et al. concluded that implementing robots
in rehabilitation would improve the cost-effectiveness of interventions. This discrepancy
can result from differences in economies, healthcare systems, and remuneration of healthworkers between Mexico and the UK. Another explanation of differences between those
two studies can result from different algorithms of cost-effectiveness calculations. Bustamante Valles et al. [29] provided a detailed formula that was based on total expenses (yearlywage of one therapist, cost of purchase, and maintenance of robotic device) divided by the
number of patients whom a single therapist was able to treat during one year. Rodgers et al.
did not provide such details of their calculations. Another factor that could have impacted
the cost-effectiveness of both interventions was a slight difference in setting. Rodgers et al.
provided a robotic intervention in which one therapy assistant delivered a training program.
The assistant was also supervised by a senior therapist, which required more staff than the
Bustamante Valles et al. study. In the Bustamante Valles et al. study, one therapist was able
to deliver therapy to a number of patients at the same time because patients were exercisingon six stations of computer- and motor-assisted devices. Stations were distributed in acircle, which allowed the therapist to move between patients easily. As Carpino et al. [24]pointed out in a meta-analysis regarding lower limb rehabilitation after stroke, with andwithout robotic interventions, to measure the realistic cost of rehabilitation, trials should bedesigned differently than in the available literature. One should set a target for the patientsto achieve instead of applying the same duration of rehabilitation with or without robotic
devices. Such a scenario may show that with the use of robotic devices, rehabilitation goals
will be achieved earlier in the traditional rehabilitation process, thus lowering the costof patient rehabilitation (including side costs such as transportation, social care, hospitalstay, and nursing care costs). Carpino et al. showed that rehabilitation using robots ismore effective than classic rehabilitation, which is not confirmed by our present research.
This may be due to the fact that these authors, in their meta-analysis, focused on robotic
interventions for the lower limbs, while in our study, the robotic intervention concerned
the upper limbs.
Some other authors found that robot-assisted rehabilitation can be more efficient than
traditional rehabilitation. Dehem et al. [30] conducted a study on 28 stroke patients inwhich traditional upper limb rehabilitation was compared with combined rehabilitation
and robotic-assisted therapy. The group that received combined therapy exhibited better
improvement in terms of gross manual dexterity and upper limb ability during various
functional tasks, while abilities to perform manual activities and activities of daily living
presented similar improvements in both groups. The authors stated that rehabilitation
combined with robot-assisted exercises was more effective than rehabilitation alone. Similar
results were obtained by Daunoraviciene et al. [31] in a study conducted on 34 stroke
patients, which aimed to identify the effect of robot training on the functional recovery of
the arm. The authors used a device that was in the form of an orthosis with a system ofsprings to support the patient’s upper limb. The device also had sensors that continuously
assessed the quality and range of the patient’s movement during training sessions andadjusted the degree of limb relief depending on the correctness of the performance ofthe motor task. The device did not use any actuators. The authors found that the study
group (which underwent rehabilitation with additional robot-assisted exercises) presented
significant improvement in upper extremity motor function compared to the control group(which underwent rehabilitation and occupational therapy sessions instead of robotic

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10 of 13intervention). The abovementioned authors showed that robotic interventions in theirstudies were superior to traditional rehabilitation, which is inconsistent with our results.
However, Dehem et al. showed that despite better improvement in the robotic groupin terms of manual dexterity, the ability to perform manual tasks and activities of dailyliving improved similarly in both groups. This is partially in line with our results. The
difference between the cited studies and the present one may result from the fact that in thepresent study, we did not use a detailed measurement of hand dexterity in terms of various
functions.
As stated previously, the majority of available studies using robots in rehabilitation were conducted on stroke patients; however, a few authors compared using rehabilitation robots with conventional rehabilitation on patients with different conditions.
Kawasaki et al. [32] and Pool et al. [33] assessed robotic assistive gait training for children
with cerebral palsy. Kawasaki et al. included 11 children in their study; however, oneof the participants resigned from the study because of the inability to accommodate thelaboratory setting. The assistive device used in their trial comprised two tight frames thatwere attached to the patient’s legs and two actuators placed lateral to hip joints, which
helped produce flexion and extension torque during the gait. The authors reported thatin the studied group (n = 10), two parameters of gait–limb symmetry and the positive
peak of the anterior–posterior ground reaction force improved, yet no change in gait speed
was observed. Pool et al. used a different device in their study. Their robotic-assisted
gait training used electrical stimulation of the participant’s muscles, helping them to actively increase the potential to withstand weight load by helping with a contraction of
the participant’s own muscles. The authors demonstrated that adding robot-assisted gait
training to locomotor training did not improve the gait performance of cerebral palsy
patients. Both examples showed that different approaches are used to improve patients’ability to move. Robotic devices are used not only to deliver external force to help startand maintain movement but also can improve muscle function by electrical stimulation. It
is worth pointing out that Kawasaki et al. included children with Gross Motor Function
Classification System (GMFCS) scores at levels I to III while Pool et al. included children
with GMFCS levels III to V. This may further lead to different outcomes as the studiedgroups were at different physical performance levels at the baseline. Different results wereobtained by Yildirim et al. [34] for patients with spinal cord injury. The authors used an
exoskeleton with four motors and a computer. During the exercises, the device is capable ofreducing the patient’s weight load, allowing the patient to walk as the weight of the patientis reduced. At the end of the training, patients were walking with full body weight (noassistance). The authors found that adding such robot-assisted gait training to rehabilitation
improved the outcomes of gait reeducation in the studied group.
According to the available literature, rehabilitation using robots shows either similar effectiveness to classic rehabilitation, which is consistent with our results, or higher
effectiveness than classic rehabilitation. The cost-effectiveness of this type of intervention
remains an open issue that requires further research.
To our knowledge, the current study is the first clinical trial to compare the effectiveness of traditional neurological rehabilitation and neurological rehabilitation combinedwith a rehabilitation robot for patients with post-COVID-19 fatigue syndrome. We consider
this as a strength of this study. However, the study has some limitations. Due to the setting,
we are not able to calculate the realistic cost-effectiveness of the intervention because participants had to undergo the whole rehabilitation process, and rather than achieving thepredetermined goal, their improvement was measured. A further limitation results fromorganizational and financial reasons, due to which only exercises of the upper limb were
performed using the EMG-driven robotic device.
Human–robot interaction (HRI) is studied mainly in the area of social robotics. HRI
may be different when a non-humanoid rehabilitation robot is used, like the one in thecurrent study. Hertz and Wieze [35] showed that people tend to choose a human to interact
with instead of a robot or screen applications. They also found that persons’ acceptanceSensors 2023, 23, 8120

11 of 13of a robot’s advice and compliance with its guidelines increased when robots looked
more human-like. Tobis et al. demonstrated that a real-world interaction with a robot
improved factors of its acceptance [36]. On the other hand, it has been established thatpeople who suffer from physical illness are often subject to psychological distress [37], andactions conducted by physiotherapists to support and empower the patients are pointed
out as important and beneficial in terms of physical and mental health [33,34,37,38]. In thepresent study, we measured neither the HRI nor the patients’ perceptions of robotic therapy.
Psychological factors such as mood and self-acceptance were not assessed. Therefore, we
consider this a third limitation. Further investigations should take into consideration how
replacing an individual training with a physiotherapist with exercises on robotic devices
impacts both the physical and mental domains of the patient. Another limitation is theunequal sample size. Differences in the size of groups result from the fact that we used a
simple randomization. The study initially included a few more participants in both groups
than required because we expected dropouts. However, differences in group sizes in thepresent study were small, and such differences are common in clinical trials even whengroups are initially equal in size. The last limitation is that upper limb weakness was not
an inclusion criterion. As most of the patients transferred from the ICU have some levelof muscle weakness, we did not specify this criterion [39]. Different levels of weakness
may potentially bias the results of the study; however, there were no statistical differences
between the studied groups in terms of upper limb strength.
5. Conclusions
In light of the results of the present study, implementing EMG-driven robots in the
rehabilitation of post-COVID-19 fatigue syndrome patients is effective. Substituting a partof traditional rehabilitation with robotic rehabilitation did not negatively alter rehabilitation
outcomes. Our results indicate that the implementation of robotic rehabilitation for patientswith post-coronavirus fatigue syndrome in rehabilitation programs can be supported.
Further investigations aiming at achieving rehabilitation goals rather than completinga rehabilitation period should be conducted in the future to establish the realistic costeffectiveness of such procedures.
Author Contributions: Conceptualization, E.Z., D.K., S.T. and K.H.; formal analysis, T.T. and A.P.
(Anna Pieczyńska); investigation, R.M.; methodology, T.T., D.K. and I.Z.; project administration, R.M.
and E.Z.; resources, K.H., M.M. and A.P. (Anna Poświata); supervision, K.H.; writing—original draft,
T.T.; writing—review and editing, T.T., S.T., A.P. (Anna Pieczyńska), E.Z., K.H., M.M., A.P. (AnnaPoświata) and I.Z. All authors have read and agreed to the published version of the manuscript.
Funding: This research and the APC were funded by the National Center for Research and Development of Poland grant No. POIR.01.01.01-00-2077/15.
Institutional Review Board Statement: The study was conducted in accordance with the Declarationof Helsinki and approved by the Bioethics Committee of Poznan University of Medical Sciences(Protocol No. 514/21).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The authors confirm that the data supporting the findings of this study
are available from the authors upon reasonable request.
Acknowledgments: The authors wish to thank the EGZOTech company for providing the Luna EMG
robot free of charge.
Conflicts of Interest: D.K., M.M. and A.P. (Anna Poświata) are employees of the EGZOTech company,
which designed the Luna EMG robot.

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