









































J Global Clinical Engineering Vol.7 Issue 1: 2025 52

Received February 8, 2024, accepted February 10, 2025, date of publication March 17, 2025.

Review

Effectiveness of Robotic Rehabilitation in the Management of 
Stroke Patients—A Literature Review

Manav Prasad, Deepshikha Madhual, Kriti Sachan*, Afshan Perwez, Shivani Tiwari and Baldev Negi

1 Department of Physiotherapy, Sharda School of Allied Health Sciences, Greater Noida, Uttar Pradesh, India.

* Corresponding Author Email: kriti.sachan@sharda.ac.in

ABSTRACT

Background and Objective: Stroke is considered a root cause of disability worldwide, adversely affecting movement and 
balance. It requires comprehensive rehabilitation to achieve maximum recovery. Gait training, including robot-assisted methods, 
is crucial in restoring independence among stroke survivors. Balance impairment leads to challenges that demand specialized 
interventions, while cognitive deficits add complexity to rehabilitation. Despite ongoing research, optimizing outcomes remains 
a challenge, urging innovation in trial design and intervention strategies to enhance the effectiveness during stroke rehabilita-
tion. This literature review highlights the evidence regarding the uses and effectiveness of robotic rehabilitation amongst stroke 
survivors. Methods: The searches were performed on databases like PubMed, Scopus, and Google Scholar using keywords such 
as gait, balance, cognitive ability, and upper limb rehabilitation. The inclusion criteria were the studies published in English 
with a study design of randomized controlled trials focusing on stroke patients. The intervention included robotic rehabilita-
tion. Qualitative data synthesis was gathered after screening the abstracts and full texts of the included articles. Result: This 
literature review found that robotic rehabilitation, including intensive and personalized sessions, targeted resistance, augmented 
feedback, and sensory inputs, yields significant improvements across multiple domains for stroke patients. These improvements 
include enhanced gait parameters, balance, cognitive abilities, and upper limb functionality. Robotic-assisted therapy can im-
prove motor function, coordination, memory, attention, and sensory perception, ultimately contributing to better recovery and 
quality of life for individuals affected by stroke. Conclusion: This study concluded that combining robotic rehabilitation with 
other techniques can provide enhanced benefits compared to conventional rehabilitation. However, more studies are required 
to reach any firm conclusion.

Keywords—Stroke, Gait, Robotic rehabilitation, Upper limb rehabilitation, Cognitive ability, Balance. 

           
Copyright © 2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - Attribu-
tion 4.0 International - CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) 
are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is 
permitted which does not comply with these terms.



53 J Global Clinical Engineering Vol.7 Issue 1: 2025

Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

INTRODUCTION

Stroke is the preliminary cause of disability observed 
amongst adults, leading to substantial financial conse-
quences for victims, their families, and society as a whole. 
Following a stroke, disabilities are a hedge to healthcare 
and have several long-term counteraccusations on a per-
son’s capability to ambulate and maintain balance.1 The 
unexpected reduction in brain conditioning causes weak-
ness in one side of the lower extremities. Such individuals 
tend to depend more on the lower extremities, which are 
unaffected. They are more likely to have an inconsistent, 
unstable distribution of weight and a reduced gait cycle.2 
Thus, perfecting and recovering the capacity to walk is 
essential to gaining autonomy in day-to-day conditioning 
and perfecting daily life quality.1 The general physical 
state and the strength, endurance, and coordination of 
their lower extremities amongst stroke survivors can be 
improved with gait training. Advancements in muscle tone 
normalization, balance, overall fitness and endurance, and 
functional skills are all included in the Barthel Index (BI) 
and Rivermead Mobility Index. These scales are accepted 
as suitable criteria to assess a stroke case’s functional 
condition and are reliable labels of the effectiveness of 
the enforced therapy.3

Numerous strategies, including neurodevelopmental 
procedures, repeated task training, biofeedback, bodyweight-
supported treadmill training, robot-supported training, 
and high-intensity physical therapy, have been used in 
neurorehabilitation programs to enhance balance and 
locomotor capabilities. Despite these initiatives, opinions 
on how well these approaches enhance balance and motor 
skills are still undiscovered.1 One technique utilized to 
assist stroke victims in recovering their capacity to walk 
is robot-supported gait training. It enables the creation 
of walking movements continually, adding the number of 
gait cycles and step accuracy while requiring trainers to 
deliver the least amount of physical effort. An exoskeleton-
assisted robot is generally used in robot-assisted gait 
training, which may be divided into two primary types: 
over-ground and treadmill-based exoskeleton robots.4  

Amongst stroke victims, balance damage is a serious 
concern that can arise from several causes, including 
defined range of motion, muscular atrophy, sensitive 

abnormalities, and cognitive issues. This impairment 
makes movement delicate and raises the possibility of 
falling. The inability to integrate sensitive data from the 
vestibular, visual, and somatosensory systems is a major 
contributing factor. Balance is maintained by somatosensory 
signals from the lower extremities in healthy individu-
als, though stroke victims frequently do not receive this 
information. Balance requires central integration, which 
is the activation of substitute sensitive systems to make 
up for inadequacies. Balance capability can be enhanced 
using specialized training methods, like modifying sensory 
inputs or measuring balance with analytical equipment. 
However, studies on how stroke survivors’ center of pres-
sure movement and muscle activation are impacted by 
sensory integration.5

 One of the most common physical impairments leading 
to stroke-related disability that affects the performance of 
daily living activities is gait disorder, which is a common 
clinical issue for stroke survivors. Therefore, a primary 
focus of post-stroke rehabilitation is gait disorder.6

Following a stroke, patients walk with coordinated 
lower extremities mass patterns instead of controlled 
movement of individual joints. Walking induces two kinds 
of synergistic patterns. While the hip, knee, and ankle 
dorsiflexors produce the mass flexion pattern during the 
swing phase, the quadriceps and gluteus maximus work 
in concert to produce a mass extension pattern during the 
stance phase. Basic deficits causing asymmetry include 
poor support for a single limb and uncontrollably moving 
forward. Reduced stance time and extended swing dura-
tion on the affected side make up the asymmetry. The 
gait cycle’s regular pattern of symmetrical step length is 
absent, with the paretic side having a longer way.7

 Post-stroke cognitive impairment is the term used 
to describe cognitive deficits that manifest three to six 
months following a stroke. The stroke itself can cause these 
deficits, or they can pre-exist. Aphasia, memory problems, 
and advanced-order cognitive dysfunctions similar to 
executive and visuospatial impairments are among these 
deficits; these frequently coincide with vascular cognitive 
impairment. Studies have demonstrated cognitive decline 
both before and after stroke, and vascular risk factors raise 
the threat of both stroke and cognitive decline.8



Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

J Global Clinical Engineering Vol.7 Issue 1: 2025 54

The need for stroke rehabilitation services is rising 
as strokes continue to be the primary cause of adult dis-
ability. Numerous large-scale intervention trials aimed 
at motor recovery report similar advancements in motor 
performance for both the intervention and control groups, 
though not always to the same degree. These indifferent 
outcomes could result from the tested interventions’ lack 
of added benefit or the numerous difficulties in planning 
and carrying out extensive stroke rehabilitation trials. New 
approaches to patient selection, control interventions, 
and endpoint measures are strategies for enhancing the 
quality of trials. Rehabilitation techniques help stroke 
survivors recover their independence indeed, though 
research into stroke rehabilitation aims to enhance trials, 
interventions, and results.9

The main objective of this review study was to sum-
marize robotic rehabilitation’s effectiveness in managing 
stroke patients. This study provides valuable insight into 
the promising benefits of robot-assisted rehabilitation for 
improving the quality of life among individuals suffering 
from stroke.

METHODS   

Search strategy: A comprehensive literature search was 
conducted across multiple databases, including keywords 
such as “robotic rehabilitation”, “stroke”, and “rehabilita-
tion”. The articles were searched in different databases 
including PUBMED, GOOGLE SCHOLAR, PEDRO, and 
COCHRANE LIBRARY. 

Inclusion criteria: Inclusion criteria for study selection 
involved Randomized Controlled Trials and Pilot Studies 
published between 2017 and 2024. 

Exclusion criteria: Exclusion criteria excluded systematic 
reviews, meta-analyses, and articles published before 2017. 

Data extraction: Initial searches identified 87 relevant 
articles. These articles underwent screening, with 30 
identified for this review study. The included articles 
compared outcomes of robotic rehabilitation interventions 
versus control groups in stroke patients. Data collection 
encompassed various parameters, such as the impact of 
robotic training on gait, balance, cognitive ability, and up-
per limb rehabilitation. Additionally, different components 

demonstrating the efficacy of robotic rehabilitation were 
reviewed.

RESULT  

Effect of Robotic Rehabilitation on Gait

Kim et al. (2024) conducted a study titled “Simultaneous 
High-Definition Transcranial Direct Current Stimulation 
(HD-tDCS) and Robot-Assisted Gait Training in Stroke 
Patients”.10 The research utilized the Lokomat robotic 
device and involved 24 participants. These patients were 
split into the Real HD-tDCS set, and the Sham HD-tDCS 
set. In this Real HD-tDCS set, participants obtained robotic 
training alongside transcranial direct current stimulation, 
whereas the Sham HD-tDCS set underwent robotic drill-
ing without the stimulation. Assessments using various 
measures such as the Functional Ambulation Category 
(FAC), Dynamic Gait Index (DGI), Fugl-Meyer Assessment 
(FMA), Timed-Up-and-Go (TUG) test, Berg Balance Scale 
(BBS), 10-Meter Walk Test (10MWT), Functional Reach 
Test, Visual Analog Scale (VAS), and Korean Modified BI 
(K-MBI) were conducted. The action spanned 10 sessions 
over four weeks. After four weeks, significant improve-
ments were observed in all test parameters within the Real 
HD-tDCS set, whereas the Sham HD-tDCS set displayed 
no notable improvement. The Real HD-tDCS set exhibited 
multiple enhancements among physical functions, indi-
cating the positive impact of combining robotic training 
with transcranial direct current stimulation.

Li et al., conducted a study titled “Effect of Robot-
Assisted Gait Training on Motor and Walking Function in 
Patients with Subacute Stroke”.4 The research utilized the 
BEAR-H1 (wearable lower extremity exoskeleton robot) 
robotic equipment and included 36 patients aged 18 to 75. 
Patients were separated into two clusters: Cluster A, the 
experimental cohort, and Cluster B, the baseline cluster, 
which were delivered traditional therapy. Assessments 
were conducted using measures such as FAC, Mini-Mental 
State Questionnaire, Ashworth test, 6-Minute Walk as-
sessment (6MWT), Functional Ambulatory Classification, 
Fugl-Meyer questionnaire for bottom extremity, and Modi-
fied Ashworth Scale. Both groups underwent exercises 
focusing on muscle strengthening, stretching, and balance 
for four weeks, twice a day for 1,800 seconds, five days 



55 J Global Clinical Engineering Vol.7 Issue 1: 2025

Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

intervention comprised sessions conducted thrice a week, 
with each session lasting 30 minutes, spanning over seven 

weeks. Additionally, the intervention group received an 
extra 20 minutes of treatment with the G-EO System dur-
ing each session. After seven weeks, participants in both 
groups demonstrated improvements in various aspects, 
including reduced risk of falling, increased walking speed, 
decreased fear of falling, improved mobility, and enhanced 
performance in daily tasks. Notably, the group utilizing 
the G-EO system experienced further advantages, such as 
improved walking speed, better balance, reduced fear of 
falling, and increased acceptance of technological aids.12

Alingh et al., conducted a study titled “Training for 
Improvement of Propulsion Symmetry and Gait Speed in 
Chronic Stroke Patients”.13 The study utilized the LOPES Ⅱ, 
Demcon and MOOG BV, USA robotic devices. A total of 29 
participants between 51–71 years old were interviewed 
for the research. The study consisted of a single group 
that received treatment using the LOPES Ⅱ robotic device. 
Assessments were conducted using assessment tools such 
as the Hospital Anxiety and Depression Scale, Modified 
Ashworth Test, FMA, Functional Gait Assessment, Stroke 
Impact Assessment, Mini-Mental State Test, Medical Re-
search Council (MRC) scale, 6-Minute Walk Test (6MWT), 
Star Cancellation Test, MI, and FAC. The exercise duration 
for the group was comprised of sessions conducted twice 
a week, with each session lasting 60 minutes and spanning 
over five weeks. After five weeks of treatment, participants 
experienced improved balance and coordination in walk-
ing, stronger leg movements, increased ankle flexibility 
on the weaker side, and enhanced overall walking speed, 
balance control, arm function, and cognitive abilities.13

Heng et al., in 2020, conducted a study titled “Changes 
in Balance, Gait, and Electroencephalography after Robot-
Assisted Gait Training in Chronic Stroke Patients”.14 The 
study utilized the MRG-P 100 HIWIN Robotic Gait Train-
ing System, India and included 24 partakers between 35 
and 80 years. The survey involved the Traditional Group 
and the Robot-Assisted Gait Training (RAGT) group. The 
Traditional Group received standard physiotherapy re-
habilitation, whereas the RAGT group received standard 
physiotherapy and robotic gait training. Assessments were 
conducted using the Berg Balance Assessment and the 
Timed “Up and Go” test. The intervention for both groups 
consisted of sessions conducted four times a week, with 
every sitting lasting 30–45 minutes, spanning over four 

out of seven days. After four weeks, improvements were 
observed in motor abilities, gait performance, and walking 
endurance in patients treated with BEAR-H1 compared 
to those receiving conventional therapy. This proposes 
that Robot-acquired Gait Training is more effective for 
people with subacute stroke.4

Longatelli et al. conducted a study titled “Robotic Exo-
skeleton Gait Training in Stroke”.11 The study utilized robotic 
devices such as Ekso, Re-Walk, and Indego and included 
29 contributors between 18–80 years old. Contributors 
were segregated into two bunches: The Control Bunch 
(CB), which received standard rehabilitation methods, 
and the Experimental Bunch (EB), which underwent a 
combination of conventional therapy and rehabilitation 
using an exoskeleton device. Assessments were conducted 
using the Modified Barthel Scale, Motricity Index, 10-meter 
walk test, 6-minute walk assessment, Functional Ambula-
tory Category, and Trunk Control Test. The intervention 
consisted of sessions conducted five times a week, each 
lasting 60 minutes, spanning over four weeks. Both groups 
demonstrated progress in their abilities (Capacity Score) 
after four weeks of intervention. The EB progress has 
been comparable to that of the CB after the experiment, 
with minor improvements observed in lower leg muscle 
activity during walking measurements.11

Maranesi et al. conducted a study titled “Robotic Inter-
vention for Older Patients with Subacute Stroke”.12 The 
study incorporated the G-EO System which is a robotic and 
the end-effector device aiding in walking therapy. Over 
152 subjects, 65 years and above, have been involved in 
research. The study comprised the control group and a 
technology-based experimental set. The control group 
underwent a standard rehabilitation program, while 
the intervention group engaged in a robotic rehabilita-
tion program utilizing the G-EO system alongside their 
conventional therapy. Assessments were conducted us-
ing measures such as the FAC, Modified Ashworth test, 
Short Form-12 (SF-12), Performance-Oriented Mobility 
test, Motricity Index (MI), Mini-Mental State Test, River-
mead Assessment, Barthel Scale, Clinical Dementia Rat-
ing, Activities-specific Balance Confidence Scale (ABC), 
Participation in Autonomy and Domestic Life, and gait 
analysis along with instrumental postural analysis. The 



Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

J Global Clinical Engineering Vol.7 Issue 1: 2025 56

weeks. Additionally, after the standard duration, the RAGT 
set acquired an extra 30 minutes of robotic gait exercise. 
After four weeks, the RAGT therapy resulted in a four-fold 
increase in balance improvements compared to usual care, 
indicating its superior effectiveness and potential added 
benefits in treating the condition.14

 Kotov conducted a study titled “Robotic Restoration 
of Gait Function in Elderly Patients with Stroke”.15 The 
study utilized the ExoAtlet exoskeleton and Ortorent 
MOTO pedal trainer, Italy. A subtotal of 47 participants 
between the age of 52 and 74 were incorporated into the 
experiment. Participants were split into two bunches: 
ExoAtlet exoskeleton bunch, which received rehabilita-
tion using the provided robotic device, and the Ortorent 
MOTO pedal trainer group, which underwent dynamic and 
ideal training for all extremities using the pedal trainer. 
Assessments were conducted using the MRC assessment, 
Modified Ashworth test, Berg Balance test, Hemiplegic Arm 
Shoulder Ability (HASA), 10-Meter Walk Test (10MWT), 
modified Rankin assessment, and BI. The exercise dura-
tion for both groups consisted of sessions conducted five 
days a week, each lasting 10–30 minutes, reliant on the 
participants’ functional capacity, over two weeks. After 
two weeks, both groups experienced improvements in 
strength, balance, mobility, and walking pace. However, 
Group 1, utilizing the ExoAtlet exoskeleton, significantly 
improved more than Group 2. Group 1 also demonstrated 
reduced disabilities and increased daily function, which 
were more pronounced than those observed in Group 2. 
These findings suggest that both robotic training methods 
effectively improve gait and balance, with the ExoAtlet 
exoskeleton showing particular efficacy.15

Nolan et al., in the year 2020 conducted a study titled 
“Robotic Exoskeleton Gait Training During Acute Stroke 
Rehabilitation”.16 The study utilized a Robotic exoskeleton 
(Indigo Powered Exoskeleton) and involved 22 contribu-
tors within the customary age set of 59.6 years. The study 
comprised two groups of participants: the RE (Robotic 
Exoskeletons) +SOC (conventional Standard of Care) 
Group, which underwent robotic exoskeleton (RE) gait 
training as a component of their inpatient recovery pro-
gram, and the conventional Standard of Care Set, which got 
standard rehabilitation treatments during their inpatient 
rehabilitation program. Assessments were conducted 

using the Modified Functional Classification, Modified 
Functional Evaluation, Walking Functional Classification, 
and Functional Independence Measure (FIM). The inter-
vention consisted of sessions conducted thrice a week, 
each lasting 25 minutes, spanning over four weeks. Both 
groups demonstrated improvement in movement abilities 
after four weeks, but the RE+SOC group exhibited greater 
improvements than the SOC bunch. The RE+SOC bunch 
could engage in more intense walking practice without 
extending their training time, resulting in better recovery 
of their ability to perform daily tasks.16

Kim et al., conducted a study titled “Robotic-Assisted 
Gait Training for Balance and Lower Extremity Function in 
Patients with Infratentorial Stroke”.17 The study employed 
the Lokomat robotic orthosis and WALKBOT Mechanical-
aided walking therapy and involved 19 participants with 
an average age of 47.4 years. Contributors have been 
divided into sets: Set A and Set B. Set A underwent four 
weeks of Resistance Agility Grappler Training combined 
with Cognitive Processing Therapy (CPT), after four weeks 
of CPT alone. In contrast, Set B received interventions 
oppositely: four weeks of CPT ensured by four weeks of 
RAGT combined with CPT. Conducted assessments using 
measures such as the Trunk Impairment Test, Fugl-Meyer 
Assessment for Lower Extremity (FMA-LE), Functional 
Electrical Stimulation (FES), 10-Meter Walk Test (10MWT), 
BBS Test, Scale for the Assessment and Rating of Ataxia 
(SARA), and FAC. The intervention consisted of sessions 
conducted five times a week, each lasting 30 minutes over 
four weeks. After a month, both groups demonstrated 
significant progress in maintaining balance while mov-
ing and standing still, lower body movement abilities 
(measured by FMA-LE), and coordination (measured by 
SARA). However, the group that underwent RAGT com-
bined with conventional physical therapy (PT) showed a 
distinct advantage in maintaining balance while standing 
compared to the group receiving conventional PT alone. 
Additionally, while both groups showed improvements in 
walking ability (measured by FAC), the RAGT+CPT group 
showed more significant improvement in static balance 
(measured by BBS), and upper body movement abilities 
(measured by FMA-UE) improved slightly in both groups.17

Kim et al., examined the effects of “Effects of robot-
assisted gait training for stroke patients”18, utilizing 



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Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

robotic devices including the Gait Trainer, Lokomat, 
Chicago, United States, and Morning Walk, Korea. The 
study comprised 25 participants, with a mean age of 57.7 
years in the trial cluster and 60.4 years in the traditional 
cluster. The research compared two cohorts: The Morning 
Walk®, Korea Group, where participants underwent 30 
minutes of Techno-assisted walking rehabilitation with 
Morning Walk® along with 60 minutes of conventional PT 
per session, and the traditional cluster, which solely got 
90 minutes of traditional PT. Evaluation tools employed 
encompassed the Modified Barthel Scale, Rivermead 
Mobility scale, Functional Ambulatory Category score, 10 
Meter Walk examination, Berg Balance test, and MI for 
lower extremities (Motricity Index-Lower). The exercise 
regimen entailed sessions five times a week, each span-
ning 60 minutes, over three weeks. After completing the 
three-week treatment, both groups exhibited significant 
enhancements across all measured parameters. Notably, 
the Morning Walk® group demonstrated more pronounced 
improvements in leg movement (quantified by the Motric-
ity Index-Lower score) and balance (evaluated through 
the BBS) than the control group. Moreover, both cohorts 
displayed advancements in walking speed (indicated by 
increased pace in the 10 Meter Walk assessment) and 
balance (as evidenced by elevated scores on the BBS.18

Effect of Robotic Rehabilitation on Balance

 Giovannini, et al., conducted a survey titled “Robotic-
Assisted Rehabilitation for Balance and Gait in Stroke 
Patients”.19 The study utilized the Hunova Movendo 
Technology srl robotic device, Italy, robotic platform, 
end-effector RAGT, and robotic balance platform. A total 
of 24 partakers having a mean age of 65 years were col-
lected in the investigation. The investigation involved The 
Investigative Cluster (IC) and Regulation Cluster (RC). The 
partakers in the IC underwent specialized balance disor-
der rehabilitation using a robotic platform in addition to 
standard care. At the same time, those in the RC received 
only traditional treatment as per their daily routine, 
without the robotic platform intervention. Assessments 
were conducted using measures such as Motricity Scale 
for lower extremity, Short Physical Performance Battery 
(SPPB), Berg Balance test, TUG test, ABC (Activities- spe-
cific Balance Confidence) Scale, Walking Handicap test, 
FAC, 10-Meter Walk Test (10MWT), 6-Minute Walk Test 

(6MWT), Barthel Index for Modified Kitchens (BIMK), 
EQ-5D-5L questionnaire (EQ-50), Modified Fatigue Im-
pact Scale (MFIs), Fatigue Severity Scale (FSS), Frontal 
Assessment Battery (FAB), Symbol Digit Modalities Test 
(SDMT), Digit Cancellation Test, Trail Making Test (TMT), 
and Tinetti Assessment Measure. The duration of the ex-
ercises was thrice a week, for 45 minutes, spanning over 
four weeks. At the end of the four-week intervention, both 
groups demonstrated improved balance, fatigue levels, 
quality of life, and physical and mental abilities. It was 
anticipated that the group receiving robotic-assisted 
therapy and regular therapy (Investigative Cluster) would 
show greater effectiveness than the group receiving only 
regular therapy (Regulation Cluster).19

Li et al., investigated a trial on “Effects of a Brain-
Computer Interface-Operated Lower Limb Rehabilita-
tion Robot on Motor Function Recovery in Patients with 
Stroke”.20 In this study, Brain-Computer Interface (BCI) 
technology was employed. Twenty-eight patients were 
taken in the trial with an average age of three and seven 
decades. Two groups were established: the BCI cluster 
and the Sham cluster. The BCI cluster received robotic 
exercise, physiotherapy, and medical treatments, while 
the Sham group only received physiotherapy and medical 
treatment. Assessment tools such as Levels of Cognitive 
Functioning Test for Adults, FMA-UE (Fugl-Meyer As-
sessment for Upper Extremity), FAC, MBI (Modified BI), 
Serum Brain-Derived Neurotrophic Factor (BDNF) Levels, 
FMA-LE (Fugl-Meyer Assessment for Lower Extremity) 
and neurophysiological variables incorporating Motor 
Evoked Potential latency and amplitude were utilized. 
The exercise regimen consisted of sessions conducted 
six days a week, each lasting 30 minutes, spanning four 
weeks. After four weeks, the BCI group demonstrated 
significant improvements in various abilities for stroke 
recovery patients. Specifically, cognitive abilities showed 
enhancement, as evidenced by improved Levels of Cog-
nitive Functioning Scale (LCFS) scores indicating better 
cognitive function. While both groups exhibited similar 
improvements in upper limb motor functions, gait, and 
balance, the positive effect of BCI, especially for cognitive 
ability improvement, was highlighted.20

Chen et al., conducted a study titled “Effect of Telereha-
bilitation on Balance in Individuals with Chronic Stroke”.21 



Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

J Global Clinical Engineering Vol.7 Issue 1: 2025 58

The study utilized various robotic devices, including the 
Kinect Sensor, RAGT (Microsoft Corporation, Redmond, 
WA, USA), with a Virtual Reality (VR) system, Virtual 
Reality System, Exergaming Telerehabilitation System, 
and Interactive Self-Rehabilitation Programs. A gross 
of 30 participants with a mean age of six decades were 
enrolled in the trial. The study comprised two sets: the 
Manipulated set, which participated in a VR intervention 
program, and the Sham Set, which received traditional 
PT treatment. Assessments were conducted using the 
Berg Balance test, TUG, MI, FAC, and Modified Falls Effi-
cacy Scale. The duration of the exercise was six times for 
four weeks, for 2,400 seconds, spanning over a month. 
Within four weeks, both the Sham and Manipulated sets 
demonstrated measurable improvements in balance and 
walking. However, the Experimental Set exhibited supe-
rior balance improvements. Both sets showed enhance-
ments in BBS scores, indicating improved balance, while 
the Manipulated group notably reduced their TUG test 
times, suggesting enhanced mobility. The Manipulated 
set’s significant advancements in balance and walking 
measures compared to the Sham set establish its supe-
riority. Specifically, the Manipulated set improvements in 
BBS scores and TUG test times signify enhanced balance 
and mobility, respectively.21

De Luca et al., investigated title “Dynamic Stability and 
Trunk Control Improvements Following Robotic Balance 
and Core Stability Training in Chronic Stroke Survivors”.22 
The study utilized the robotic device Hunova. A sum of 
15 partakers in the investigation, with an average age of 
59 years old in the robotic squad and 63 years old in the 
experimental squad. The study consisted of two squads: 
The Experimental Squad, which underwent a rehabilitation 
program using robots, and the Control Squad, which under-
went conventional rehabilitation sessions led by physical 
therapists. Assessments were conducted using the BBS, 
Mini-Balance Evaluation Systems Test (Mini-BESTest), and 
Trunk Impairment Scale. The exercise duration for both 
groups was six times for four weeks, for 2,700 seconds, 
spanning five weeks. After five weeks of exercise, both 
groups demonstrated enhanced balance, walking abili-
ties, arm function, and cognitive performance. However, 
the control group only showed significant improvement 

in their ability to maintain balance when reacting to 
unexpected disturbances, while the Experimental Group 
maintained their balance improvements, as assessed 
by the BBS, over time. Specifically, for the Experimental 
Group, there was an enhanced ability to step forward 
and backward confidently, as indicated by the Mini-BES 
Test. Additionally, statistically significant improvements 
in balance as documented in Berg Balance Scale (BBS) 
persisted over time, along with increased trunk control 
and stability during activities.22

Castelli et al., conducted a study titled “Robotic-Assisted 
Rehabilitation for Balance in Stroke Patients (ROAR-S): 
Effects of Cognitive, Motor, and Functional Outcome”.23 
The study utilized the robotic device Hunova® Movendo 
Technology, srl, Genoa, Italy, a cutting-edge robot designed 
to aid in rehabilitation for core stability, balance, and 
lower body functions. This robotic platform is specifically 
designed to assess and treat the trunk and lower limbs, 
providing personalized therapy. The study involved 24 
participants with an approximate age of 77 years old in the 
Hunova Crew (HuC) and 76 years old in the Conventional 
Crew (CoC). HuC group received special treatment with 
the Hunova robotic platform for balance problems, on top 
of the usual treatment recommended by doctors. The CoC 
group served as a comparison and received only the usual 
treatment recommended by doctors. Assessments were 
conducted using measures such as the FAC, EuroQol-5D 
(EQ-5D), Modified Fatigue Impact Scale (MFIs), Fatigue 
Severity Scale (FSS), Functional Ambulation Battery, 
SDMT, TMT, Berg Balance test, SPPB, Modified BI (MBI), 
ABC scale, Walking Handicap Scale, and other cognitive 
and motor assessments. The duration of the exercise was 
thrice a week. Treatment outcomes for both groups showed 
improvements in clinical scales, cognitive performance, 
balance, mobility, quality of life, and fatigue. The HuC 
group demonstrated further enhancements in motor skills, 
cognitive function, and overall well-being compared to the 
CoC group. Both groups experienced shared improvements 
in gait, including enhanced ambulation, increased speed 
in the Timed Up & Go test, and improved walking and 
sit-to-stand abilities under the SPPB. Additionally, both 
groups showed strengthened balance, as indicated by 
improvements in the BBS and SPPB balance sub-score.23



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Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

Effect of Robotic Rehabilitation on Cognitive Ability                     

Zhao et al., conducted a study titled “Effects of Training 
with a Brain-Computer Interface Controlled Robot on Re-
habilitation Outcome in Patients with Subacute Stroke”.24 
The study employed a BCI-controlled robotic device and 
Newton’s ring to elicit Steady-State Motion Visual Evoked 
Potentials. A total of 33 participants ages 32 to 68 years 
old were taken for the experiment. Two groups formation 
took place: the Sham cluster and the BCI cluster. The Sham 
cluster received conventional physiotherapy, while the 
BCI cluster received BCI-based intelligence in addition to 
conventional physiotherapy. Assessments were conducted 
using the LOCTA, Fugl-Meyer Testing for the Lower Limb, 
FAC, FMA for the Upper Limb, Modified Barthel testing, 
and Serum BDNF levels. Both groups received therapies 
for four weeks, 1 time a day for half an hour, 12 days of 
two weeks. After a month, improvements were observed 
in cognitive function, lower limb motor function, increased 
levels of BDNF, and ambulation abilities in patients treated 
with BCI and conventional therapy compared to the Sham 
cluster. These findings suggest a positive effect of BCI in 
patients with subacute stroke.24 

Torrisi et al., organized a review on “The role of hand 
robotic rehabilitation plus VR in improving cognitive 
function”.25 In this study, the AMADEO Robotic device, 
USA was utilized. 48 participants, with a typical age of 54 
years old, were incorporated. The candidates were frac-
tioned into two bands: the Manipulated and the Standard 
bands. The Manipulated band received treatment from 
the AMADEO robot, while the Standard band underwent 
conventional PT (Physiotherapy). Assessment tools such 
as Mini-Mental State Questionnaire, TMT, Stroop Test, 
Clock Drawing Test, RAVLT (Rey Auditory Verbal Learn-
ing Test), FMA, ARAT (Action Research Arm Test), BBT, 
NHPT, Jebsen-Taylor Hand Function Test, BI, FIM, MoCA 
(Montreal Cognitive Assessment), mRS (modified Rankin 
Scale), NEADL (Nottingham Extended Activities of Daily 
Living) and SIS (Stroke Impact Scale) were utilized for 
testing. The duration of the exercise was not specified in 
the provided information. After the treatment, the study 
demonstrated that participants who received robotic 
hand therapy (RHT) experienced greater improvement 
in cognitive abilities compared to those who received 
conventional hand therapy. Specifically, AHT enhanced 

attention, executive function, and visual-spatial skills. 
However, hand function improvement was similar for 
both groups.25

Aprile et al., carried out a survey on “Robotic Rehabili-
tation to Improve Cognitive Functions in Subjects with 
Stroke”.26 In this study, three robotic models—Motore, 
Amadeo, and Diego (Tyromotion and Humanware)—along 
with a sensor-based instrument called Pablo, were uti-
lized. The study comprised 51 partakers with an average 
age of 64 years. Various cognitive assessment tools were 
employed, including the Tower of London for Executive 
Functions, SDMT for Attention and Processing Speed, 
Digit Span Task for Memory, Oxford Cognitive Screen, 
FMA for Upper Extremity, and Rey-Osterrieth Complex 
Figure Test. Participants underwent 30 sessions lasting 
45 minutes each, conducted five days a week. Following 
these sessions, improvements were observed in cognitive 
functions, upper extremity motor functions, and perfor-
mance in daily activities. This suggests that the combined 
effect of robotics and cognitive exercises contributes to 
patient recovery.26

Manuli et al., conducted a study on “Robotic Reha-
bilitation plus VR affect cognitive behavioral outcome 
in patients with chronic stroke”.27 This study used Com-
puter Assisted Reality, Lokomat Nanos, and Lokomat Pro 
robotic devices, USA. The review included a whole of 90 
individuals, with 30 individuals allocated to each group. 
Three distinct groups were established: Team 1, compris-
ing the “Robotic Rehabilitation team with VR”; Team 2, 
consisting of the “Robotic Rehabilitation without VR”; and 
Team 3, receiving “conventional therapy”. Assessment 
tools utilized in the study included the Montreal Cogni-
tive Assessment, FIM Cognitive Subscale, Motor Subscale, 
Weigl Test, Short Form-12 Health Survey Total (Mental 
and Physical), Beck Depression Inventory-Ⅱ, TMT Form, 
Visual Search and FAB. Each participant underwent 40 
sessions of their respective treatments, followed by 40 
sessions of physiotherapy. After the completion of these 
sessions, improvements were observed across all three 
groups in cognitive functioning, mood, executive functions, 
and activities of daily living (ADL). Nevertheless, Team 
1 receiving robotic rehabilitation and VR demonstrated 
impressive enhancements in shifting skills, quality of 
life, selective assessment, and cognitive flexibility. This 



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J Global Clinical Engineering Vol.7 Issue 1: 2025 60

suggests that the combination of robotic rehabilitation 
and VR provides the most effective approach to cognitive 
rehabilitation.27

Effect of Robotic Rehabilitation on Upper Limb 
Management

Frisoli et al., conducted a study on “A randomized 
clinical control study on the efficacy of three-dimensional 
upper limb RE training in chronic stroke”.28 The study 
employed the Pnew-WREX, ARMin exoskeleton, and L-
EXOS exoskeleton, Italy. Twenty-two people took part, 
segregated into two bunches: The Robotic bunch, which 
received treatment from the exoskeletons, and the CB, 
which underwent manual PT. Assessment tools such as 
BAT, FMA, and the Ashworth Scale were utilized. Exercise 
sessions were conducted thrice a week, each lasting 2,700 
seconds, 6 times for four weeks. Following the 6-week 
period, the Robotic Group exhibited significant improve-
ments in functional ability and task precision, indicating 
the positive effects of Robotic Rehabilitation compared 
to conventional therapy.28

 Takebayashi et al., handled an analysis on “Robot-As-
sisted Training as Self-Training for Upper Limb Hemiplegia 
in Chronic Stroke”.29 The study focused on the use of the 
ReoGo-J upper limb extremity equipment, Brazil. The 
study involved 129 participants aged between 58 and 60. 
Three groups were established: the baseline assembly, 
who underwent basic physiotherapy techniques with 
self-improvement methodologies; the Robot Training 
(RT) assembly, which underwent robot-assisted training 
of ReoGo-J unit before standard occupational therapy; and 
the Movement Therapy (MT) Group, wherein participants 
engaged in occupational techniques based on Constraint-
Induced Movement Therapy, task-oriented therapy, and 
robot-assisted therapy. Various assessment tools were 
utilized, including MAS, Performance Test for Upper Limb 
Functions, Motor Evaluation in Vascular Hemiplegia, Re-
search Analysis of SIS, FMA, Action Research Arm Test, 
MI for Muscle Strength, Active Range of Joint Motion As-
sessment, SIS for Quality of Life. Exercise sessions were 
conducted thrice weekly, each lasting for an hour for two 
and a half months. After the intervention, RT assembly 
demonstrated the most significant improvement in FMA-
UE scores, indicating the highest benefit. Additionally, the 

RT Group exhibited the greatest enhancement in upper 
limb function compared to the other groups.29

Budhota et al., conducted the following study on 
“Robotic Assisted Upper Limb Training in Stroke”.30 The 
study utilized the H-MAN robotic equipment, USA. Forty-
four participants, encompassing a range of ages from 21 
to 85, were encapsulated for investigation. Participants 
were fragmented into two squads: The robotic therapy 
squad, which received combined therapy of H-MAN robotic 
and conventional physiotherapy, and the conventional 
therapy (CT) squad, which received only conventional 
therapy. Assessment tools such as FMA, VAS, MAS, MMSE, 
LTA, CTA, ARAT, and GS were employed. The RT squad 
underwent 60 minutes of H-MAN training, after half an 
hour of traditional techniques, at the same time, the CT 
squad received one and a half hours of traditional tech-
niques. Both squads participated in sessions lasting 90 
minutes each, three sessions a week for one and a half 
months. After the 6-week experiment, participants in 
the RT squad showcased growth in motor function and 
movement smoothness compared to the CT squad. Ad-
ditionally, combination therapy reduced the workload 
demand on therapists.30

Shi et al., conducted a study on “Effects of a Soft Ro-
botic Hand for Hand Rehabilitation in Chronic Stroke 
Survivors.”31 The study utilized the VAEDA robotic device. 
Sixteen participants aged 56, were collected in the search, 
which consisted of a single group. Assessment tools such 
as BBT, MAS, FMA-UE, ARAT, and Maximum Voluntary 
Grip Strength test were employed. Exercise sessions 
were conducted seven days a week, with every session 
approximating 60 minutes, over six weeks. After a 6-week 
intervention, a significant improvement in test scores was 
observed, indicating the effectiveness of robotic exercises 
for hand rehabilitation in chronic stroke survivors.31

Li et al., conducted a study titled “Efficacy of Robotic 
Priming with Bilateral Approach in Stroke Rehabilitation”.32 
The research employed the Bi-Manu-Trace robotic device 
and involved 31 participants having a mean age of 55. Two 
groups were formed: the Robotic Primed Mirror Therapy 
crew (RMT) and the Robotic Primed Bilateral Upper Limb 
Training crew (RBULT). RMT crew participants underwent 
robotic training and mirror therapy, whereas in the RBULT 



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Patients—A Literature Review

group, participants received robotic training and bilateral 
upper limb training. Assessments were conducted using the 
robotic Neurological Severity Test, Chedoke Arm and Hand 
Activity Inventory, and accelerometer. The intervention 
consisted of sessions conducted six times in two weeks, 
with each training lasting 2,400 to 2,700 seconds over six 
weeks. After six weeks, the research findings indicated 
that the group receiving robotic priming with MT dem-
onstrated a better outcome in motor function and arm 
use when matched to the crew receiving robotic priming 
with bilateral upper limb training. Therefore, combining 
robotic training with mirror therapy may improve motor 
function and arm functionality for stroke patients.32

Guillen-Climent et al., conducted a study titled “Use of 
MERLIN in Stroke Patients: A Robotic Device Based on 
Serious Games for Upper Limb Rehabilitation in Home 
Settings”.33 This study utilized the AA Robotic device, Italy, 
MERLIN robotic device, and Arm Assist robotic system. 
There were nine engagers between the ages of 41 and 
84. The study comprised only one group, which received 
training from the MERLIN robotic system. Assessments 
using the Modified Ashworth Assessment and Fugl-Meyer 
scale were conducted. The exercise duration was thrice a 
week, for 30 minutes each session, spanning over three 
weeks. After three weeks, significant improvements were 
observed in upper limb coordination and overall motor 
function score.33

Ranzani et al., explored “Neurocognitive robot-assisted 
rehabilitation of hand function”.34 The study utilized the 
ReHapticKnob device. Thirty-three participants, covering 
ages from 18 to 19, were covered in the study, with 14 
participants in the Robotic Group and 13 in the Control 
Group. Assessment tools such as FMA-UE, FMA-WH, FMA-
SE, MAS, EmNSA-T, EmNSA-P, VAS, LCF-P, NIHSS, GoodGlass 
Kalpan Assessment, and Albert Test were employed. The 
Control Group underwent exercises 2–3 sessions a week 
for 30–45 minutes, whereas the Robotic Group engaged 
with set of 3, 2 times a week for 2,700 seconds. Both 
groups had kept track of assessments at 8 weeks and 32 
weeks. The study concluded that robotic training yields 
outcomes comparable to Neurocognitive therapy, suggest-
ing its potential as an alternative treatment approach for 
hand function rehabilitation.34

Aprile et al., executed a study, “Upper Limb Robotic 
Rehabilitation After Stroke”.35 In this study, various 
robotic devices were utilized: Motore, a robotic device 
facilitating assisted and unassisted flat motion of elbow 
and shoulder joints; Amadeo, supporting assisted and 
unassisted bending and straightening movements of fin-
gers; Pablo, a sensor-based system enabling independent 
three-dimensional motion of wrist, shoulder, and elbow 
joints; and Diego, a device aiding three-dimensional, 
one/two-handed motion of the shoulder joint with arm 
weight assistance. The study encompassed a total of 224 
members between the ages of 4 and 85, segregated in 
two sets: The Robotic set (RS), undergoing therapy with 
robotic devices targeting shoulder, elbow, hand, and wrist 
joints, and the Conventional set (CS), receiving traditional 
treatment focusing on upper limb function improvement, 
sensorimotor control restoration, and muscle stiffness 
reduction. Assessment tools such as FMA, MI, MRC, MAS, 
DN4, NRC, mRI, FAT, ARAT, SF 36-PCS, and SF-36-MCS 
were employed. Treatment comprised daily 45-minute 
episodes, five times a week, over the month, for both 
sets. Additionally, conventional rehabilitation sessions 
occurred six times a week, each lasting 45 minutes, dur-
ing the same month. After four weeks of treatment, both 
the Robotic set and the Conventional set demonstrated 
improvement in several areas. The average FMA score 
increase was 8.50 for RS and 8.57 for CS, surpassing the 
clinically meaningful improvement threshold of 5 points. 
RG exhibited greater enhancement in upper extremity 
strength, as calculated by the Motricity Test, compared 
to CS, and maintained this advantage at the treatment’s 
conclusion.35

Huang et al., reviewed “The comparison of the rehabili-
tation effectiveness of neuromuscular electrical stimula-
tion robotic hand training and pure robotic hand training 
after stroke”.36 In this study, a variety of robotic devices 
were employed, including the Hybrid neuroprosthesis for 
the upper extremity, robotic hand, EMG-Driven robotic 
hand, EMG-driven neuromuscular electrical stimulation 
(NMES) robotic hand, and electromechanical wrist robot 
assistive system. Fifteen engagers represented the age of 
57 for the experimental team and 6 decades for the pure 
team, were contained in the review. The study encom-
passed two participant cohorts: the NMES cohort, which 



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J Global Clinical Engineering Vol.7 Issue 1: 2025 62

utilized a robotic hand controlled by electromyography 
(EMG)-driven NMES, and the Pure cohort, which utilized 
a robotic hand without additional NMES stimulation. As-
sessment tools such as FIM, MAS, ARAT, and FMA were 
employed. The exercise regimen consisted of sessions 
conducted thrice a week for 30 minutes over three months. 
After three months, it was observed that neuromuscular 
electrical stimulation (NMES) improved hand function 
in paralyzed patients when contrasted to a pure cohort 
without stimulation. NMES cohort exhibited a notable 
increase in hand function assessment scores (HFAS) and 
a substantial reduction in elbow, wrist, and finger muscle 
stiffness. The NMES group maintained these improvements 
in hand function, whereas the control group’s hand function 
assessment score declined at the 12-week follow-up. The 
review emphasizes on upper limb function, particularly 
hand function, demonstrated significant benefits from 
NMES correlated to the control team.36

Franceschini et al., facilitated an examination on 
“Upper limb robot-assisted rehabilitation versus PT on 
subacute stroke patients”.37 In this study, robotic devices 
were employed, namely the InMotion2 robotic system 
and Planer end-effector robots. A total of 48 participants 
were involved, where the robotic crew is 74 years old 
and the conventional crew with an average age of 7. 
Involved parties were fragmented into two crews: The 
Experimental Crew, utilizing InMotion2 robotic system, 
Chicago  for upper body rehabilitation, involving goal-
based, two-dimensional reaching tasks, and the control 
crew, receiving conventional upper body PT. CT activities 
included stretching assistance, arm and shoulder training, 
and reaching activities with therapist guidance. Assess-
ment tools such as FMA for upper extremity, lax range of 
motion, Modified Ashworth test for shoulder stiffness, and 
Modified Ashworth Scale for elbow stiffness were utilized. 
The exercise regimen consisted of sessions conducted five 
times a week, each lasting 45 minutes, spanning over six 
weeks. After six weeks, both crews illustrated improved 
upper extremity working, as assessed by the Fugl-Meyer 
measurement. Additionally, the Experimental Group im-
proved shoulder and elbow stiffness (measured by the 
Modified Ashworth Scale) and arm flexibility (measured 
by passive range of motion). The Experimental Crew il-
lustrated superior improvement in these areas compared 

to the Control crew, which only showed improvement in 
shoulder stiffness.37

Qian et al., conducted a study on “Early Stroke Rehabilita-
tion of the Upper Limb Assisted with an Electromyography-
Driven Neuromuscular Electrical Stimulation-Robotic 
Arm”.38 This study utilized various robotic devices, includ-
ing the EMG-driven NMES robotic arm, Rehabilitation 
robot ARMin Ⅱ, USA. Electromyography-driven robot, 
and electromechanical wrist robot-assisted system de-
vice. Twenty-four participants had a typical age of 54 for 
the exploratory cluster and 6.4 decades for the Control 
cluster. The study comprised two participant groups: the 
NMES-Robot Group, which underwent training using a 
robotic arm delivering NMES, and the Control cluster, 
which received conventional rehabilitation treatments 
focused on the upper limb. Assessment tools such as the 
Action Research Arm Examination, Function Independence 
Assessment, Modified Ashworth Scale and Fugl-Meyer 
Examination were employed. The exercise regimen 
consisted of sessions conducted five times a week, each 
lasting 40 minutes, spanning over four weeks. Following a 
month of drill, the exploratory cluster (NMES robot) and 
the control cluster exhibited substantial improvements in 
FMA, MAS, ARAT, and FIM. Nevertheless, the NMES-robot 
cluster demonstrated significantly grander improvements 
in FMA scores, particularly for the wrist and hand. This 
improvement has not been seen for control cluster, dis-
playing superior efficacy of NMES-RT in enhancing wrist 
and hand function.38

DISCUSSION  

The current literature review critically investigated 30 
articles to highlight the effects of robotic rehabilitation 
on stroke. In addition to the basic impact of traditional 
physiotherapy in the form of manual techniques and a 
basic exercise program that was approved as an effective 
modality for the improvement of gait, balance, cognition 
and upper limb this study investigates for the beneficial 
effects of robotic rehabilitation for stroke survivors. Bruni 
et al.’s research highlighted significant improvements in 
gait parameters.39 They emphasized the importance of 
patients engaging in more intense and repetitive train-
ing sessions, enhancing brain flexibility and supporting 
motor recovery. Task-Oriented Training through robotic 



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Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

rehabilitation offers personalized sessions tailored to each 
patient’s specific needs, focusing on enhancing particular 
motor skills and functional movements to improve overall 
mobility. Additionally, certain robotic systems provide 
augmented feedback, crucial in enhancing motor learn-
ing and performance by offering immediate feedback on 
movement quality and progress. As a result, robotic gait 
rehabilitation can effectively enhance walking speed, bal-
ance, and coordination, ultimately leading to improved 
gait function and greater independence in daily tasks.39 
Similarly, the reviewed articles examined various gait 
parameters, including step and stride length, gait speed, 
cadence, motor skills, and functional ability. Moreover, 
notable improvements were observed in gold-standard 
assessment scales such as the BBS, 10-Minute Walk Test, 
and Time-Up-and-Go test. Specific scales like the Fugl 
Mayer Assessment and DGI were also used to assess and 
track progress accurately. Zheng et al.’s research highlights 
the improvement in balance parameters, emphasizing 
that enhancing muscle strength is a key benefit of robot-
acquired training for patients suffering from a stroke.40 
This training provides targeted resistance and controlled 
movements, enhancing balance function. Additionally, 
coordination improves as patients are guided through 
various tasks and exercises, helping them relearn and 
refine the motor skills necessary for balance control. The 
Recurring and work-oriented quality of training with robot-
acquired training promotes neural plasticity, enabling the 
brain Restructure and establish fresh neural pathways, 
thereby aiding in balance function improvement during 
the recovery process. The therapy also offers patients a 
variety of sensory inputs, including proprioceptive and 
vestibular feedback, crucial for maintaining balance and 
spatial awareness.40

 Moreover, postural control can be enhanced in stroke 
patients through robotic assistance, targeting specific 
muscle groups and adjusting their center of gravity, es-
sential for maintaining balance during various activities. 
The aforementioned articles discussed balance param-
eters such as static and dynamic balance and ambulation. 
Additionally, improvements were noted in parameters 
like swing amplitude, center of pressure, and speed of 
oscillation. Significant improvements were observed 
in gold-standard scales such as the BBS and Fugl Meter 
Balance Scale. The research by Aminov et al., highlighted 

significant improvements in cognitive abilities, underscor-
ing the potential advantages of robotic rehabilitation in 
enhancing cognitive function among stroke patients.41 
For example, VR interventions show promise in boosting 
cognitive function and memory by leveraging the con-
nection between motor skills and cognitive capabilities. 
However, further in-depth investigations are necessary 
to fully understand the extent of these benefits and refine 
treatment plans for cognitive rehabilitation using robotic 
technology.41

The articles above discuss improvements in major 
components such as attention, visuomotor skills, memory, 
cognitive flexibility, executive functions, shifting skills, and 
enhancements in LOCTA score. Additionally, improvements 
in mood were also observed.

Bertani et al’s research sheds light on the significant 
improvement in upper limb functionality.42 They highlight 
how robotic therapy holds promise in enhancing motor 
function recovery in the upper limb, especially for indi-
viduals grappling with chronic strokes. Positive reorga-
nization in the motor cortex can lead to better outcomes 
in arm function. Additionally, advanced robotics assisting 
in therapy through focused and repetitive exercises can 
greatly expedite recovery after a brain injury, improving 
upper limb function. Robotic technology can also enhance 
flexor synergies, coordination, and speed in the affected 
upper limb while improving the sense and understanding 
of the shoulder, arm, and forearm. Moreover, robotic-
assisted therapy can alleviate joint pain in the upper limb, 
enhancing comfort and mobility during rehabilitation.42

The articles discussed above underscore improvements 
in various parameters of the upper limb, including motor 
functions, coordination, and sensory function. Significant 
enhancements were noted in gold-standard assessment 
scales such as the Fugl Meyer Assessment, Action Reach 
Arm Test, and Modified Ashworth Scale. Furthermore, 
additional scales such as the Biconical Activity test and 
MI were also utilized, highlighting the comprehensive 
evaluation of upper limb functionality.

Robotic rehabilitation for stroke patients has been 
extensively studied recently, with research post—2018 
highlighting its feasibility and potential benefits. Feasibil-
ity studies have demonstrated the practicality of robotic 



Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
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J Global Clinical Engineering Vol.7 Issue 1: 2025 64

interventions in various settings. For instance, a 2018 
pilot study evaluated the use of a robotic glove for hand 
rehabilitation in hemiplegic stroke patients at home. The 
findings indicated that the intervention was both feasible 
and safe, with 81% of participants completing the program. 
Significant improvements were observed in hand motor 
function, dexterity, and strength. Similarly, research from 
2020 assessed the use of a single-joint Hybrid Assistive Limb 
(HAL-SJ) robot for upper limb rehabilitation in subacute 
stroke patients with varying severity levels. This study 
concluded that robot-assisted rehabilitation is feasible 
across different severity groups, with the most notable 
improvements in patients with moderate impairments.

The efficacy of robotic rehabilitation is further supported 
by studies integrating multiple therapeutic modalities. A 
2021 study introduced the perSonalized UPper Extremity 
Rehabilitation (SUPER) program, which combined robotics, 
VR, and NMES. This program, tailored to individual func-
tional levels, demonstrated feasibility and effectiveness, 
with 64% of participants showing clinically significant 
improvements in upper extremity function. Additionally, 
recent developments in neural interface technology, such 
as Neuralink’s BCI trials, have explored controlling robotic 
arms through brain implants. While primarily targeting 
individuals with paralysis, this technology holds promising 
implications for stroke rehabilitation by enabling direct 
neural control of assistive devices.

This study has several limitations, including limited 
access to the databases, leading to the inclusion of fewer 
studies. Secondly, a quality appraisal of the included stud-
ies was not performed.

Future recommendations include high-quality ran-
domized controlled trials to reach any firm conclusion 
regarding the effectiveness of robotic rehabilitation in 
the resolution of post-stroke survivors’ symptoms.

CONCLUSION  

In conclusion, recent research underscores the feasibil-
ity and safety of robotic rehabilitation for stroke patients, 
with significant functional improvements and high patient 
compliance. Advancements in integrating robotics with 
other modalities and neural interface technologies further 
enhance the potential of robotic rehabilitation in stroke 

recovery. The motive behind this study was to show that 
including robotic rehabilitation with other techniques can 
result in similar advantages to rigorous training. Analysis 
of the existing data indicates that robotic therapy can im-
prove walking, balance, thinking, memory, coordination, 
daily tasks, motor abilities, and posture management. In 
the end, all of these areas may experience enhancements 
by implementing robotic rehabilitation. However, more 
studies are required to confirm the existing findings.

AUTHOR CONTRIBUTIONS

All the authors contributed equally in the conduct of 
the review study.

ACKNOWLEDGMENTS
Sincere thanks and gratitude towards the faculty 

members (Kriti Sachan and Baldev Negi) of Department 
of Physiotherapy, Sharda School of Allied Health Sciences, 
Sharda University, Greater Noida, Uttar Pradesh, India. 
They provided their valuable insights regarding the con-
ceptualization of the topic, article selection, data analysis 
and final drafting of the manuscript. 

FUNDING
This review has not received any funds through any 

agency.

CONFLICTS OF INTEREST
There is no conflict of interest as reported by the authors.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE
The study was conducted in accordance with the ethi-

cal guidelines.

FURTHER DISCLOSURE
Not applicable. 

REFERENCES
1. Aprile, I., Conte, C., Cruciani, A., et al. Efficacy of RAGT 

combined with robotic balance training in subacute 
stroke patients: a randomized clinical trial. J Clin 
Med. 2022;11(17):5162. https://doi.org/10.3390/
jcm11175162.



65 J Global Clinical Engineering Vol.7 Issue 1: 2025

Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

2. Kim, S.Y., Yang, L., Park, I.J., et al. Effects of innovative 
WALKBOT robotic-assisted locomotor training on 
balance and gait recovery in hemiparetic stroke: a 
prospective, randomized, experimenter blinded case 
control study with a four-week follow-up. IEEE Trans 
Neural Syst Rehabil Eng. 2015;23(4):636–642. https://
doi.org/10.1109/TNSRE.2015.2404936.

3. Rojek, A., Mika, A., Oleksy, Ł., et al. Effects of exoskeleton 
gait training on balance, load distribution, and func-
tional status in stroke: a randomized controlled trial. 
Front Neurol. 2020;10:1344. https://doi.org/10.3389/
fneur.2019.01344.

4. Li, D.X., Zha, F.B., Long, J.J., et al. Effect of Robot Assisted 
Gait Training on Motor and Walking Function in Patients 
with Subacute Stroke: A Random Controlled Study. J 
Stroke Cerebrovasc Dis. 2021;30(7):105807. https://
doi.org/10.1016/j.jstrokecerebrovasdis.2021.105807.

5. Jang, S.H and Lee, J.H. Impact of sensory integration 
training on balance among stroke patients: Sensory 
integration training on balance among stroke patients. 
Open Med (Wars). 2016;11(1):330–335. https://doi.
org/10.1515/med-2016-0061.

6. Wang, Y.J., Mukaino, M., Ohtsuka, K., et al. Gait character-
istics of post-stroke hemiparetic patients with different 
walking speeds. Int J Rehabil Res. 2020;43(1):69–75. 
https://doi.org/10.1097/MRR.0000000000000391.

7. Verma, R., Arya, K.N., Sharma, P., et al. Understanding 
gait control in post-stroke: implications for manage-
ment. J Bodyw Mov Ther. 2012;16(1):14–21. https://
doi.org/10.1016/j.jbmt.2010.12.005.

8. Rost, N.S., Brodtmann, A., Pase, M.P., et al. Post-
stroke cognitive impairment and dementia. Circ Res. 
2022;130(8):1252–1271. https://doi.org/10.1161/
CIRCRESAHA.122.319951.

9. Stinear, C.M., Lang, C.E., Zeiler, S., et al. Advances and 
challenges in stroke rehabilitation. Lancet Neurol. 
2020;19(4):348–360. https://doi.org/10.1016/
S1474-4422(19)30415-6.

10. Kim, E., Lee, G., Lee, J., et al. Simultaneous high-
definition transcranial direct current stimulation 
and robot-assisted gait training in stroke patients. 
Sci Rep. 2024;14(1):4483. https://doi.org/10.1038/
s41598-024-53482-6. 

11. Longatelli, V., Pedrocchi, A., Guanziroli, E., et al. Robotic 
exoskeleton gait training in stroke: an electromyography-
based evaluation. Front Neurorobot. 2021;15:733738. 
https://doi.org/10.3389/fnbot.2021.733738.

12. Maranesi, E., Bevilacqua, R., Di Rosa, M., et al. An in-
novative training based on robotics for older people 
with subacute stroke: study protocol for a randomized 
controlled trial. Trials. 2021;22(1):400. https://doi.
org/10.1186/s13063-021-05357-8.

13. Alingh, J.F., Groen, B.E., Kamphuis, J.F. et al. Task-specific 
training for improving propulsion symmetry and gait 
speed in people in the chronic phase after stroke: a proof-
of-concept study. J Neuroeng Rehabil. 2021;18(1):69. 
https://doi.org/10.1186/s12984-021-00858-8.

14. Heng, H,M., Lu, M.K., Chou, L.W., et al. Changes in Bal-
ance, Gait and Electroencephalography Oscillations after 
Robot-Assisted Gait Training: An Exploratory Study in 
People with Chronic Stroke. Brain Sci. 2020;10(11):821. 
https://doi.org/10.3390/brainsci10110821.

15. Kotov, S.V., Isakova, E.V., Lijdvoy, V.Y., et al. Robotic 
Restoration of Gait Function in Patients in the Early 
Recovery Period of Stroke. Neurosci Behav Physiol. 
2021;51:583–589. https://doi.org/10.1007/
s11055-021-01109-y.

16. Nolan, K.J., Karunakaran, K.K., Chervin, K., et al. Robotic 
exoskeleton gait training during acute stroke inpatient 
rehabilitation. Front Neurorobot. 2020;14:581815. 
https://doi.org/10.3389/fnbot.2020.581815.

17. Kim, H.Y., Shin, J.H., Yang, S.P., et al. Robot-assisted 
gait training for balance and lower extremity func-
tion in patients with infratentorial stroke: a single-
blinded randomized controlled trial. J Neuroeng 
Rehabil. 2019;16(1):99. https://doi.org/10.1186/
s12984-019-0553-5.



Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

J Global Clinical Engineering Vol.7 Issue 1: 2025 66

18. Kim, J., Kim, D.Y., Chun, M.H., et al. Effects of robot-
(Morning Walk®) assisted gait training for pa-
tients after stroke: a randomized controlled trial. 
Clin Rehabil. 2019;33(3):516–523. https://doi.
org/10.1177/0269215518806563. 

19. Giovannini, S., Iacovelli, C., Brau, F., et al. RObotic-
Assisted Rehabilitation for balance and gait in Stroke 
patients (ROAR-S): study protocol for a preliminary 
randomized controlled trial. Trials. 2022;23(1):872. 
https://doi.org/10.1186/s13063-022-06812-w.

20. Li, C., Wei, J.Y., Huang, X.Q., et al. Effects of a Brain-
Computer Interface-Operated Lower Limb Rehabili-
tation Robot on Motor Function Recovery in Patients 
with Stroke. J Healthc Eng. 2021;4710044. https://
doi.org/10.1155/2021/4710044.

21. Chen, S.C., Lin, C.H., Su, S.W., et al. Feasibility and effect 
of interactive telerehabilitation on balance in indi-
viduals with chronic stroke: a pilot study. J Neuroeng 
Rehabil. 2021;18(1):71. https://doi.org/10.1186/
s12984-021-00866-8.

22. De Luca, A., Squeri, V., Barone, L.M., et al. Dynamic stabil-
ity and trunk control improvements following robotic 
balance and core stability training in chronic stroke 
survivors: a pilot study. Front Neurol. 2020;11:494. 
https://doi.org/10.3389/fneur.2020.00494. 

23. Castelli, L., Iacovelli, C., Loreti, C., et al. Robotic-assisted 
rehabilitation for balance in stroke patients (ROAR-S): 
effects of cognitive, motor and functional outcomes. 
Eur Rev Med Pharmacol Sci. 2023;27(17):8198–8211. 
https://doi.org/10.26355/eurrev_202309_33580.

24. Zhao, C.G., Ju, F., Sun, W., et al. Effects of training 
with a brain-computer interface-controlled robot 
on rehabilitation outcome in patients with subacute 
stroke: A randomized controlled trial. Neurol Ther. 
2022;11(2):679–695. https://doi.org/10.1007/
s40120-022-00333-z.

25. Torrisi, M., Maggio, M.G., De Cola, M.C., et al. Beyond 
motor recovery after stroke: The role of hand robotic 
rehabilitation plus virtual reality in improving cogni-
tive function. J Clin Neurosci. 2021;92:11–16. https://
doi.org/10.1016/j.jocn.2021.07.053. 

26. Aprile, I., Guardati, G., Cipollini, V., et al. Robotic rehabili-
tation: an opportunity to improve cognitive functions 
in subjects with stroke. An explorative study. Front 
Neurol. 2020;11:588285. https://doi.org/10.3389/
fneur.2020.588285.

27. Manuli, A., Maggio, M.G., Latella, D., et al. Can robotic 
gait rehabilitation plus Virtual Reality affect cognitive 
and behavioural outcomes in patients with chronic 
stroke? A randomized controlled trial involving 
three different protocols. J Stroke Cerebrovasc Dis. 
2020;29(8):104994. https://doi.org/10.1016/j.
jstrokecerebrovasdis.2020.104994.

28. Frisoli, A., Barsotti, M., Sotgiu, E., et al. A randomized 
clinical control study on the efficacy of three-dimensional 
upper limb robotic exoskeleton training in chronic 
stroke. J Neuroeng Rehabil. 2022;19(1):14. https://
doi.org/10.1186/s12984-022-00991-y.

29. Takebayashi, T., Takahashi, K., Amano, S., et al. Robot-
assisted training as self-training for upper-limb 
hemiplegia in chronic stroke: a randomized controlled 
trial. Stroke. 2022;53(7):2182–2191. https://doi.
org/10.1161/STROKEAHA.121.037260.

30. Budhota, A., Chua, K.S.G., Hussain, A., et al. Robotic as-
sisted upper limb training post stroke: a randomized 
control trial using combinatory approach toward reduc-
ing workforce demands. Front Neurol. 2021;12:622014. 
https://doi.org/10.3389/fneur.2021.622014.

31. Shi, X.Q., Heung, H.L., Tang, Z.Q., et al. Effects of a soft robotic 
hand for hand rehabilitation in chronic stroke survivors. 
J Stroke Cerebrovasc Dis. 2021;30(7):105812. https://
doi.org/10.1016/j.jstrokecerebrovasdis.2021.105812.

32. Li, Y.C., Lin, K.C., Chen, C.L., et al. A comparative efficacy 
study of robotic priming of bilateral approach in stroke 
rehabilitation. Front Neurol. 2021;12:658567. https://
doi.org/10.3389/fneur.2021.658567.

33. Guillén-Climent, S., Garzo, A., Muñoz-Alcaraz, M.N., 
et al. A usability study in patients with stroke using 
MERLIN, a robotic system based on serious games for 
upper limb rehabilitation in the home setting. J Neuro-
eng Rehabil. 2021;18(1):41. https://doi.org/10.1186/
s12984-021-00837-z.



67 J Global Clinical Engineering Vol.7 Issue 1: 2025

Prasad, Madhual, Sachan, Perwez, Tiwari, Negi: Effectiveness of Robotic Rehabilitation in the Management of Stroke 
Patients—A Literature Review

34. Ranzani, R., Lambercy, O., Metzger, J.C., et al. Neurocog-
nitive robot-assisted rehabilitation of hand function: a 
randomized control trial on motor recovery in subacute 
stroke. J Neuroeng Rehabil. 2020;17(1):115. https://
doi.org/10.1186/s12984-020-00746-7.

35. Aprile, I., Germanotta, M., Cruciani, A., et al. Upper limb 
robotic rehabilitation after stroke: a multicenter, random-
ized clinical trial. J Neurol Phys Ther. 2020;44(1):3–14. 
https://doi.org/10.1097/NPT.0000000000000295.

36. Huang, Y.H., Nam, C.Y., Li, W.M., et al. A comparison of 
the rehabilitation effectiveness of NMES robotic hand 
training and pure robotic hand training after stroke: 
A randomized controlled trial. Biomed. Signal Process. 
Control. 2020;56:101723. https://doi.org/10.1016/j.
bspc.2019.101723.

37. Franceschini, M., Mazzoleni, S., Goffredo, M., et al. Up-
per limb robot-assisted rehabilitation versus physical 
therapy on subacute stroke patients: A follow-up study. 
J Bodyw Mov Ther. 2020;24(1):194–198. https://doi.
org/10.1016/j.jbmt.2019.03.016. 

38. Qian, Q.Y., Hu, X.L., Lai, Q., et al. Early stroke rehabilitation 
of the upper limb assisted with an electromyography-
driven neuromuscular electrical stimulation-robotic arm. 
Front Neurol. 2017;8:447. https://doi.org/10.3389/
fneur.2017.00447. 

39. Bruni, M.F., Melegari, C., De Cola, M.C., et al. What does 
best evidence tell us about robotic gait rehabilitation 
in stroke patients: a systematic review and meta-
analysis. J Clin Neurosci. 2018;48:11–17. https://doi.
org/10.1016/j.jocn.2017.10.048.

40. Zheng, Q.X., Ge, L., Wang, C.C., et al. Robot-assisted 
therapy for balance function rehabilitation after 
stroke: A systematic review and meta-analysis. Int J 
Nurs Stud. 2019;95:7–18. https://doi.org/10.1016/j.
ijnurstu.2019.03.015.

41. Aminov, A., Rogers, J.M., Middleton, S., et al. What do 
randomized controlled trials say about virtual reha-
bilitation in stroke? A systematic literature review 
and meta-analysis of upper-limb and cognitive out-
comes. J Neuroeng Rehabil. 2018;15(1):29. https://
doi.org/10.1186/s12984-018-0370-2.

42. Bertani, R., Melegari, C., De Cola, M.C., et al. Effects of 
robot-assisted upper limb rehabilitation in stroke pa-
tients: a systematic review with meta-analysis. Neurol 
Sci. 2017;38(9):1561–1569. https://doi.org/10.1007/
s10072-017-2995-5.


