Novel robotic rehabilitation in Bangladesh: A narrative review

Authors

DOI:

Keywords

robotic rehabilitation, neurorehabilitation, Bangladesh, stroke, spinal cord injury, LMICs  

Correspondence

Md. Israt Hasan
Email: isratpmr@gmail.com

Publication history

Received: 23 Oct 2025
Accepted: 15 Dec 2025
Published online: 19 Dec 2025

Responsible editor

Reviewers


Funding

None

Ethical approval

Not applicable

Trial registration number

Not applicable

Copyright

© The Author(s) 2025; all rights reserved. 
Published by Bangabandhu Sheikh Mujib Medical University (currently Bangladesh Medical University).
Abstract

Background: Robotic rehabilitation has emerged as a transformative innovation in physical medicine, enabling high-intensity, task-specific, and measurable therapy that enhances neuroplasticity and functional recovery. This review summarises global evidence on robotic rehabilitation and examines its relevance and implementation challenges in low- and middle-income countries (LMICs), with a particular focus on the pioneering experience of the Bangladesh Medical University.

Methods: This narrative review synthesised literature from PubMed, Scopus, Web of Science, and Google Scholar published between January 2010 and September 2025. Included sources comprised reviews, meta-analyses, randomised controlled trials, observational studies, and policy documents addressing effectiveness, implementation, workforce, and health-system integration of robotic rehabilitation in LMICs. Evidence was thematically synthesised, prioritising higher-level studies, without formal PRISMA procedures or structured risk-of-bias assessment, consistent with accepted narrative review methodology.

Results: Global evidence supports robotic rehabilitation, with strongest benefits in stroke, moderate evidence in spinal cord injury, and emerging data in traumatic brain injury, neurodegenerative, paediatric, and musculoskeletal conditions. Effectiveness improves when robotics complement conventional therapy. In LMICs, adoption is hindered by financial, infrastructural, and workforce limitations. Bangladesh faces high disability burden and service gaps; the BMU Robotic Rehabilitation Centre represents a significant advancement in equitable, technology-driven rehabilitation.

Conclusion: Robotic rehabilitation offers measurable improvements in function and independence across diverse conditions. Strengthening infrastructure, workforce capacity, and policy support is essential for sustainable adoption in LMICs. The Bangladesh Medical University model demonstrates a feasible pathway for integrating advanced rehabilitation technologies in resource-constrained

Key messages
The Robotic Rehabilitation Centre at Bangladesh Medical University, the nation’s first university-affiliated facility for advanced rehabilitation, integrates high-intensity robotic therapy to improve outcomes for neurological and musculoskeletal disorders. Despite challenges of cost and access, it fosters research, innovation, and training, demonstrates a sustainable and technology-driven rehabilitation within LMIC settings.
Introduction

Rehabilitation medicine is transforming with robotic technologies that overcome limitations of conventional therapy, including therapist fatigue, limited intensity, and variability, enabling sustained, high-dose, task-specific training essential for neurological and musculoskeletal recovery. Robotic rehabilitation addresses many of these limitations by enabling standardised, intensive, repetitive, and data-driven therapy that can be individually tailored and objectively monitored. Devices such as exoskeletons, end-effector systems, robotic gait trainers, and sensor-based assistive platforms facilitate structured practice and real-time feedback, thereby supporting neuroplasticity and functional recovery [1-3].

Globally, disability remains a major public health concern. Stroke continues to be the leading cause of adult disability worldwide, while spinal cord injury (SCI), traumatic brain injury (TBI), neurodegenerative disorders, and musculoskeletal conditions contribute substantially to long-term functional impairment and reduced quality of life. The World Health Organization (WHO) estimates that more than one billion people live with some form of disability, with the greatest burden borne by low- and middle-income countries (LMICs) [4]. These regions face a dual challenge of rising non-communicable diseases and injury-related disability alongside constrained health-system resources.

Over the past two decades, robotic rehabilitation has been extensively studied in high-income countries (HICs). Evidence from systematic reviews and randomised controlled trials demonstrates improvements in upper-limb motor function, gait recovery, balance, and independence in activities of daily living when robotic interventions are combined with conventional therapy [5-8]. However, the translation of these advances into LMIC settings has been limited. Barriers include shortages of trained rehabilitation professionals, inadequate infrastructure, high acquisition and maintenance costs of robotic devices, lack of insurance coverage, and low public awareness of rehabilitation as a core component of health care.

Bangladesh exemplifies these challenges. Despite a high and growing burden of disability, rehabilitation services remain underdeveloped and unevenly distributed. In this context, Bangladesh Medical University (BMU) has established the country’s first university-affiliated robotic rehabilitation centre. This initiative represents a significant institutional response to rehabilitation inequities and provides an opportunity to examine the feasibility, implementation, and early experience of robotic rehabilitation in a resource-constrained setting.

Methods

This study is a narrative review integrating peer-reviewed literature and relevant grey sources. Searches were conducted in PubMed, Scopus, Web of Science, and Google Scholar for publications between January 2010 and September 2025. Search terms included combinations of robotic rehabilitation, robot-assisted therapy, neurorehabilitation, stroke rehabilitation, spinal cord injury, LMIC rehabilitation, and Bangladesh rehabilitation.

Eligible sources included narrative and systematic reviews, meta-analyses, randomised controlled trials, large observational studies, and policy or guideline documents addressing clinical effectiveness, implementation, cost, workforce development, or health-system integration of robotic rehabilitation. Engineering-focused studies without clinical application, isolated case reports, and non-English publications without English abstracts were excluded.

Evidence was synthesised thematically, with emphasis on the strength and consistency of findings across conditions and relevance to LMIC contexts. Higher-level evidence (systematic reviews and meta-analyses) was prioritised where available. Formal PRISMA procedures, duplicate screening, or structured risk-of-bias scoring were not applied, in keeping with narrative review methodology.

Results

Global evidence on robotic rehabilitation

Robotic rehabilitation has evolved from experimental prototypes to clinically established tools across neurological, musculoskeletal, paediatric, and geriatric rehabilitation. The strength of evidence varies by condition, with the most robust data available for stroke, moderate evidence for SCI, and emerging evidence for other disorders.

Stroke rehabilitation

Stroke rehabilitation represents the most extensively studied application of robotic technologies. A large Cochrane review involving more than 7,000 participants demonstrated that electromechanical and robotic-assisted arm training significantly improves activities of daily living and upper-limb motor strength compared with usual care [2]. Robotic-assisted gait training has also been shown to improve walking independence, speed, and endurance, particularly in the subacute phase and when combined with body-weight support [6-8].

Randomised trials indicate that robotic therapy can deliver treatment intensities that are difficult to achieve with conventional therapy alone, while maintaining high patient motivation through interactive feedback. Importantly, robotic interventions appear most effective when integrated into comprehensive rehabilitation programmes rather than used as standalone treatments.

Table 1 Gait and lower limb rehabilitation systems

Device names

Functions

Indications

Contraindications

ZEPU-AI2 (Upper extremity feedback training)

Repeated exercise training with proprioceptive feedback

Stroke, SCI, TBI, MS, Parkinson’s, CP, orthopedic recovery, frozen shoulder

Acute fracture, tumors, severe osteoporosis, severe shoulder pain, pacemakers

ZEPU-AI6 Plus (3D Upper limb training system)

Active/passive 3D rehab (front-back, side-side, up-down)

Stroke, SCI, TBI, arthritis, CRPS, CP, prosthesis training, Adhesive capsulitis

Unstable fractures, tumors, severe spasticity, pacemakers, sever pain, TB, local infection

ZEPU-K2000D (Upper limb trainer)

Active/passive training for recovery

Stroke, TBI, orthopedic recovery, COPD, OA

Cardiopulmonary dysfunction, limb tumors, cognitive impairment

ZEPU-SG1 Plus (Hand function comprehensive training system )

Finger and hand function recovery

Stroke, SCI, CP, nerve injuries, RA, burns, MS, PD

Open wounds, unhealed fractures, severe cramps

ZEPU-K2000A (Upper/lower limb trainer)

Active/passive training, combined limbs

Stroke, CP, SCI, PD, post-fracture rehab, ICU deconditioning

Severe cardiopulmonary dysfunction, skin damage, severe joint deformities,

open bleeding wounds

SCI indicates spinal cord injury; TBI, traumatic brain injury; MS, multiple sclerosis; CP, cerebral palsy; COPD, chronic obstructive Pulmonary disease; OA, osteoarthritis; PD, Parkinson’s disease; ICU, intensive care unit; TB, tuberculosis

SCI

Exoskeleton-assisted walking has become a promising avenue in SCI rehabilitation. Evidence suggests improvements in cardiovascular endurance, bone density, and trunk control. Sale et al. [9] highlighted the role of robotic gait therapy in reducing secondary complications such as osteoporosis and pressure ulcers. A systematic review by Miller et al. [10] concluded that exoskeletons improved functional ambulation in selected SCI patients, although long-term independence remained limited by injury severity. Nonetheless, patient satisfaction and quality of life outcomes were notably improved.

TBI

Compared with stroke and SCI, robotic rehabilitation in TBI has received less research attention. Emerging studies demonstrate improvements in gait symmetry, balance, postural control, and endurance following robotic gait training. Upper-limb robotic interventions show potential benefits in motor coordination and functional independence, although evidence remains limited to small trials and pilot studies [11]. Larger, well-designed studies are needed, particularly in LMICs where TBI burden is substantial due to road traffic accidents and occupational injuries.

Neurodegenerative disorders

Robotic rehabilitation is increasingly applied in neurodegenerative conditions such as Parkinson’s disease and multiple sclerosis. In Parkinson’s disease, robotic gait training reduces freezing episodes, improves stride length, and enhances balance [12]. In MS, robotic interventions improve walking speed, endurance, and fatigue resistance [13]. While evidence is less robust than for stroke, these findings support a complementary role for robotics in managing progressive neurological disorders.

Cerebral palsy

In paediatric cerebral palsy, robotic exoskeletons and robotic treadmills enable repetitive, engaging, task-specific training that is difficult to achieve manually. Studies demonstrate improvements in gait patterns, muscle strength, and gross motor function, particularly when robotic therapy is combined with conventional physiotherapy [14]. Robotic devices may also enhance motivation and adherence in children through interactive and gamified interfaces.

Table 2 Upper limb rehabilitation systems

Device names

Functions

Indications

Contraindications

ZEPU-AI2 (Upper extremity feedback training)

Repeated exercise training with proprioceptive feedback

Stroke, SCI, TBI, MS, Parkinson’s, CP, orthopedic recovery, frozen shoulder

Acute fracture, tumors, severe osteoporosis, severe shoulder pain, pacemakers

ZEPU-AI6 Plus (3D Upper limb training system)

Active/passive 3D rehab (front-back, side-side, up-down)

Stroke, SCI, TBI, arthritis, CRPS, CP, prosthesis training, Adhesive capsulitis

Unstable fractures, tumors, severe spasticity, pacemakers, sever pain, TB, local infection

ZEPU-K2000D (Upper limb trainer)

Active/passive training for recovery

Stroke, TBI, orthopedic recovery, COPD, OA

Cardiopulmonary dysfunction, limb tumors, cognitive impairment

ZEPU-SG1 Plus (Hand function comprehensive training system )

Finger and hand function recovery

Stroke, SCI, CP, nerve injuries, RA, burns, MS, PD

Open wounds, unhealed fractures, severe cramps

ZEPU-K2000A (Upper/lower limb trainer)

Active/passive training, combined limbs

Stroke, CP, SCI, PD, post-fracture rehab, ICU deconditioning

Severe cardiopulmonary dysfunction, skin damage, severe joint deformities,

open bleeding wounds

SCI indicates spinal cord injury; TBI, traumatic brain injury; MS, multiple sclerosis; CP, cerebral palsy; COPD, chronic obstructive pulmonary disease; OA, osteoarthritis; PD, Parkinson’s disease; ICU, intensive care unit; TB, tuberculosis

Musculoskeletal and orthopaedic rehabilitation

Robotic rehabilitation is increasingly used in musculoskeletal and post-operative care, including joint replacement, ligament reconstruction, and shoulder rehabilitation. These devices facilitate early mobilisation, graded loading, and precise range-of-motion control. Systematic reviews report reduced pain, improved joint mobility, and faster return to functional activities compared with standard therapy alone [15-17].

Cost-effectiveness and evidence gap

Although robotic rehabilitation requires substantial upfront investment, long-term benefits such as reduced disability, fewer complications, and decreased caregiver burden may render it cost-effective in high-burden conditions like stroke [3]. However, robust cost-effectiveness data from LMICs are lacking. Across conditions, effect sizes are often modest, device heterogeneity complicates comparisons, and long-term sustainability of gains remains uncertain.

Rehabilitation landscape in LMICs including Bangladesh

Despite a high burden of disability, rehabilitation services in LMICs remain underdeveloped. WHO estimates that more than 2.4 billion people could benefit from rehabilitation, the majority residing in LMICs [18]. Yet rehabilitation typically receives less than 2% of national health budgets, with services concentrated in urban tertiary centres [19]. Shortages of trained physiatrists, physiotherapists, occupational therapists, and speech therapists further limit access [20-22]. Out-of-pocket expenditure dominates health financing, and insurance coverage for rehabilitation is minimal [23]. In LMICs, stigma surrounding disability, low prioritisation of rehabilitation, and gender norms particularly restricting women’s mobility and access to household resources significantly limit rehabilitation utilisation. In parallel, high device costs, limited technical expertise, unreliable electricity, poor internet access, and low digital literacy constrain adoption of robotic and tele-rehabilitation technologies [24-27].

In Bangladesh, stroke prevalence exceeds 11 per 1,000 population, contributing substantially to disability-adjusted life years lost [28]. Road traffic accidents and industrial injuries add to the burden of SCI and TBI. Musculoskeletal disorders, including osteoarthritis and low back pain, are leading causes of chronic disability. Despite this burden, Bangladesh has fewer than 400 registered physiatrists, and specialised rehabilitation centres are largely confined to Dhaka [29-31]. Community-based rehabilitation programmes exist but remain fragmented and underfunded [32]. Rehabilitation is not fully integrated into primary health care, and awareness remains low, particularly among women and rural populations [33-36].

Recent developments including endorsement of WHO Rehabilitation 2030, inclusion of rehabilitation in national policy documents, and expansion of telemedicine following the COVID-19 pandemic offer opportunities to strengthen rehabilitation delivery [37-38].

BMU robotic rehabilitation centre

Established in 2025, the BMU Robotic Rehabilitation Centre is the first university-affiliated facility of its kind in Bangladesh. The centre aims to integrate advanced rehabilitation technologies into clinical service delivery, education, and research. Its key functions include:

  • Access: Introduction of advanced robotic rehabilitation previously unavailable in the country. This has improved access beyond affluent populations.

  • Capacity building: Training of postgraduate medical students, physiatrists, and rehabilitation therapists.

  • Research: Generation of local evidence on feasibility, outcomes, and implementation.

The centre houses 62 devices, among them 57 are robotic rehabilitation devices, and 22 are AI-enabled, covering upper-limb, lower-limb, multi-joint, and early-mobilisation applications.

Robotic therapy is delivered using a hybrid care model, complementing conventional physiotherapy and occupational therapy. Typical sessions involve 30–40 minutes of robotic training integrated into individualised rehabilitation plans based on functional status, affordability, and family support.

BMU has initiated observational data collection using validated outcome measures such as the Functional Independence Measure, Fugl–Meyer Assessment, Barthel Index, and six-minute walk test. Early experience suggests high patient motivation and acceptability, although formal effectiveness and cost-effectiveness analyses are ongoing.

Table 3 Multi-joint and whole-body rehabilitation systems

Variables  

Frequency (%)

Indication of colposcopy

 

Visual inspection of the cervix with acetic acid positive

200 (66.7)

Abnormal pap test

13 (4.3)

Human papilloma virus DNA positive

4 (1.3)

Suspicious looking cervix

14 (4.7)

Others (per vaginal discharge, post-coital bleeding)

69 (23.0)

Histopathological diagnosis

Cervical Intraepithelial Neoplasia 1

193 (64.3)

Cervical Intraepithelial Neoplasia 2

26 (8.7)

Cervical Intraepithelial Neoplasia 3

32 (10.7)

Invasive cervical cancer

27 (9.0)

Chronic cervicitis

17 (5.6)

Squamous metaplasia

5 (1.7)

Groups based on pre-test marks

Pretest
marks (%)

Posttest

Marks (%)

Difference in pre and post-test marks (mean improvement)

P

Didactic lecture classes

<50%

36.6 (4.8)

63.2 (9.4)

26.6

<0.001

≥50%

52.8 (4.5)

72.4 (14.9)

19.6

<0.001

Flipped classes

<50%

36.9 (4.7)

82.2 (10.8)

45.4

<0.001

≥50%

52.8 (4.6)

84.2 (10.3)

31.4

<0.001

Data presented as mean (standard deviation)

Background characteristics

Number (%)

Age at presentation (weeks)a

14.3 (9.2)

Gestational age at birth (weeks)a

37.5 (2.8)

Birth weight (grams)a

2,975.0 (825.0)

Sex

 

Male

82 (41)

Female

118 (59)

Affected side

 

Right

140 (70)

Left

54 (27)

Bilateral

6 (3)

Delivery type

 

Normal vaginal delivery

152 (76)

Instrumental delivery

40 (20)

Cesarean section

8 (4)

Place of delivery

 

Home delivery by traditional birth attendant

30 (15)

Hospital delivery by midwife

120 (60)

Hospital delivery by doctor

50 (25)

Prolonged labor

136 (68)

Presentation

 

Cephalic

144 (72)

Breech

40 (20)

Transverse

16 (8)

Shoulder dystocia

136 (68)

Maternal diabetes

40 (20)

Maternal age (years)a

27.5 (6.8)

Parity of mother

 

Primipara

156 (78)

Multipara

156 (78)

aMean (standard deviation), all others are n (%)

Background characteristics

Number (%)

Age at presentation (weeks)a

14.3 (9.2)

Gestational age at birth (weeks)a

37.5 (2.8)

Birth weight (grams)a

2,975.0 (825.0)

Sex

 

Male

82 (41)

Female

118 (59)

Affected side

 

Right

140 (70)

Left

54 (27)

Bilateral

6 (3)

Delivery type

 

Normal vaginal delivery

152 (76)

Instrumental delivery

40 (20)

Cesarean section

8 (4)

Place of delivery

 

Home delivery by traditional birth attendant

30 (15)

Hospital delivery by midwife

120 (60)

Hospital delivery by doctor

50 (25)

Prolonged labor

136 (68)

Presentation

 

Cephalic

144 (72)

Breech

40 (20)

Transverse

16 (8)

Shoulder dystocia

136 (68)

Maternal diabetes

40 (20)

Maternal age (years)a

27.5 (6.8)

Parity of mother

 

Primipara

156 (78)

Multipara

156 (78)

aMean (standard deviation), all others are n (%)

Mean escape latency of acquisition day

Groups                 

NC

SC

ColC

Pre-SwE Exp

Post-SwE Exp

Days

 

 

 

 

 

1st

26.2 (2.3)

30.6 (2.4) 

60.0 (0.0)b

43.2 (1.8)b

43.8 (1.6)b

2nd

22.6 (1.0) 

25.4 (0.6)

58.9 (0.5)b

38.6 (2.0)b

40.5 (1.2)b

3rd

14.5 (1.8) 

18.9 (0.4) 

56.5 (1.2)b

34.2 (1.9)b 

33.8 (1.0)b

4th

13.1 (1.7) 

17.5 (0.8) 

53.9 (0.7)b

35.0 (1.6)b

34.9 (1.6)b

5th

13.0 (1.2) 

15.9 (0.7) 

51.7 (2.0)b

25.9 (0.7)b 

27.7 (0.9)b

6th

12.2 (1.0) 

13.3 (0.4) 

49.5 (2.0)b

16.8 (1.1)b

16.8 (0.8)b

Average of acquisition days

5th and 6th 

12.6 (0.2)

14.6 (0.8)

50.6 (0.7)b

20.4 (2.1)a

22.4 (3.2)a

NC indicates normal control; SC, Sham control; ColC, colchicine control; SwE, swimming exercise exposure.

aP <0.05; bP <0.01.

Categories

Number (%)

Sex

 

   Male

36 (60.0)

   Female

24 (40.0)

Age in yearsa

8.8 (4.2)

Education

 

   Pre-school

20 (33.3)

   Elementary school

24 (40.0)

   Junior high school

16 (26.7)

Cancer diagnoses

 

Acute lymphoblastic leukemia

33 (55)

Retinoblastoma

5 (8.3)

Acute myeloid leukemia

4 (6.7)

Non-Hodgkins lymphoma

4 (6.7)

Osteosarcoma

3 (5)

Hepatoblastoma

2 (3.3)

Lymphoma

2 (3.3)

Neuroblastoma

2 (3.3)

Medulloblastoma

1 (1.7)

Neurofibroma

1 (1.7)

Ovarian tumour

1 (1.7)

Pancreatic cancer

1 (1.7)

Rhabdomyosarcoma

1 (1.7)

aMean (standard deviation)

Test results

Disease

Sensitivity (%)

Specificity (%)

PPV (%)

NPV (%)

Yes

No

Reid’s score ≥ 5

Positive

10

15

37.0

94.5

40.1

93.8

Negative

17

258

 

 

 

 

Swede score ≥ 5

Positive

20

150

74.1

45.0

11.8

94.6

Negative

7

123

 

 

 

 

Swede score ≥ 8

Positive

3

21

11.1

92.3

12.5

91.3

Negative

24

252

 

 

 

 

a High-grade indicates a score of ≥5 in both tests; PPV indicates positive predictive value; NPV, negative predictive value

Test

Sensitivity (%)

Specificity (%)

Positive predictive value (%)

Negative predictive value (%)

Reid’s score ≥ 5

37.0

94.5

40.0

93.8

Swede score ≥ 5

74.1

45

11.8

94.6

Swede score ≥ 8

11.1

92.3

12.5

91.3

Test

Sensitivity (%)

Specificity (%)

Positive predictive value (%)

Negative predictive value (%)

Reid’s score ≥ 5

37.0

94.5

40.0

93.8

Swede score ≥ 5

74.1

45

11.8

94.6

Swede score ≥ 8

11.1

92.3

12.5

91.3

Narakas classification

Total

200 (100%)

Grade 1

72 (36%)

Grade 2

64 (32%)

Grade 3

50 (25%)

Grade 4

14 (7%)

Complete recoverya

107 (54)

60 (83)

40 (63)

7 (14)

-

Near complete functional recovery but partial deformitya

22 (11)

5 (7)

10 (16)

6 (12)

1 (7)

Partial recovery with gross functional defect    and deformity

31 (16)

7 (10)

13 (20)

10 (20)

1 (7)

No significant improvement 

40 (20)

-

1 (1.5)

27 (54)

12 (86)

aSatisfactory recovery

bGrade 1, C5, 6, 7 improvement; Grade 2, C5, 6, 7 improvement; Grade 3, panpalsy C5, 6, 7, 8, 9, Grade 4, panpalsy with Hornon’s syndrome.

Narakas classification

Total

200 (100%)

Grade-1

72 (36%)

Grade-2

64 (32%)

Grade-3

50 (25%)

Grade-4

14 (7%)

Complete recoverya

107 (54)

60 (83)

40 (63)

7 (14)

-

Near complete functional recovery but partial deformitya

22 (11)

5 (7)

10 (16)

6 (12)

1 (7)

Partial recovery with gross functional defect    and deformity

31 (16)

7 (10)

13 (20)

10 (20)

1 (7)

No significant improvement 

40 (20)

-

1 (1.5)

27 (54)

12 (86)

aSatisfactory recovery

bGrade 1, C5, 6, 7 improvement; Grade 2, C5, 6, 7 improvement; Grade 3, panpalsy C5, 6, 7,8,9, Grade 4, panpalsy with Hornon’s syndrome.

Variables in probe trial day

Groups

NC

SC

ColC

Pre-SwE Exp

Post-SwE Exp

Target crossings

8.0 (0.3)

7.3 (0.3) 

1.7 (0.2)a

6.0 (0.3)a

5.8 (0.4)a

Time spent in target

18.0 (0.4) 

16.2 (0.7) 

5.8 (0.8)a

15.3 (0.7)a

15.2 (0.9)a

NC indicates normal control; SC, Sham control; ColC, colchicine control; SwE, swimming exercise exposure.

aP <0.01.

Pain level

Number (%)

P

Pre

Post 1

Post 2

Mean (SD)a pain score

4.7 (1.9)

2.7 (1.6)

0.8 (1.1)

<0.001

Pain categories

    

   No pain (0)

-

1 (1.7)

31 (51.7)

<0.001

   Mild pain (1-3)

15 (25.0)

43 (70.0)

27 (45.0)

 

   Moderete pain (4-6)

37 (61.7)

15 (25.0)

2 (3.3)

 

   Severe pain (7-10)

8 (13.3)

2 (3.3)

-

 

aPain scores according to the visual analogue scale ranging from 0 to 10; SD indicates standard deviation

Surgeries

Number  

(%)

Satisfactory outcomes n (%)

Primary surgery (n=24)

 

 

Upper plexus

6 (25)

5 (83)

Pan-palsy

18 (75)

6 (33)

All

24 (100)

11 (46)

Secondary Surgery (n=26)

 

 

Shoulder deformity

15 (58)

13 (87)

Wrist and forearm deformity

11 (42)

6 (54)

All

26 (100)

19 (73)

Primary and secondary surgery

50 (100)

30 (60)

Mallet score 14 to 25 or Raimondi score 2-3 or Medical Research grading >3 to 5.

Narakas classification

Total

200 (100%)

Grade-1

72 (36%)

Grade-2

64 (32%)

Grade-3

50 (25%)

Grade-4

14 (7%)

Complete recoverya

107 (54)

60 (83)

40 (63)

7 (14)

-

Near complete functional recovery but partial deformitya

22 (11)

5 (7)

10 (16)

6 (12)

1 (7)

Partial recovery with gross functional defect    and deformity

31 (16)

7 (10)

13 (20)

10 (20)

1 (7)

No significant improvement 

40 (20)

-

1 (1.5)

27 (54)

12 (86)

aSatisfactory recovery

bGrade 1, C5, 6, 7 improvement; Grade 2, C5, 6, 7 improvement; Grade 3, panpalsy C5, 6, 7,8,9, Grade 4, panpalsy with Hornon’s syndrome.

Trials

Groups

NC

SC

ColC

Pre-SwE Exp

Post-SwE Exp

1

20.8 (0.6)

22.1 (1.8)

41.1 (1.3)b

31.9 (1.9)b

32.9 (1.8)a, b

2

10.9 (0.6)

14.9 (1.7)

37.4 (1.1)b

24.9 (2.0)b

26.8 (2.5)b

3

8.4 (0.5)

9.9 (2.0)

32.8 (1.2)b

22.0 (1.4)b

21.0 (1.4)b

4

7.8 (0.5)

10.4 (1.3)

27.6(1.1)b

12.8 (1.2)b

13.0 (1.4)b

Savings (%)c

47.7 (3.0)

33.0 (3.0)

10.0 (0.9)b

23.6 (2.7)b

18.9 (5.3)b

NC indicates normal control; SC, Sham control; ColC, colchicine control; SwE, swimming exercise exposure.

aP <0.05; bP <0.01.

cThe difference in latency scores between trials 1 and 2, expressed as the percentage of savings increased from trial 1 to trial 2

Device names

Functions

Indications

Contraindications

ZEPU-AI4 (Multi-joint constant speed training system) 

Isokinetic training and evaluation 

Post-surgical rehab. Adhesive capsulitis stroke, SCI, ACL reconstruction, sports injury rehab

Acute fractures, tumors, severe osteoporosis, cognitive impairment

Severe local inflammation, skin ulcers

ZEPU-AI7A (Upper & lower limb trainer) 

Active/passive circular training 

Stroke, SCI, Parkinson’s, MS, CP, geriatric rehab, post-COVID weakness 

Severe spasticity, unstable fractures, pacemakers 

ZEPU-DK2 (Electric rehabilitation table) 

Early mobilization, tilt and vibration 

Stroke, SCI, TBI, arthritis, geriatrics, ICU patients 

Hypotension, unstable fractures, severe heart failure, Severe joint deformities 

ZP-PTC-3 (PT Training Bed) 

Bed-based mobility, balance, transfer training 

PD, CP stroke, paraplegia, quadriplegia, ICU early mobilization, post-operative rehabilitation

Unstable angina, DVT, severe osteoporosis, severe cognitive impairment 

SCI indicates spinal cord injury; ACL, anterior cruciate ligament; MS, multiple sclerosis; CP, cerebral palsy; COVID, coronavirus disease; TBI, traumatic brain injury; ICU, intensive care unit; PD, Parkinson’s disease; DVT, deep vein thrombosis

 Lesion-size

Histopathology report

Total

CIN1

CIN2

CIN3

ICC

CC

SM

0–5 mm

73

0

0

0

5

5

83

6–15 mm

119

18

1

4

0

0

142

>15 mm

1

8

31

23

12

0

75

Total

193

26

32

27

17

5

300

CIN indicates cervical intraepithelial neoplasia; ICC, invasive cervical cancer; CC, chronic cervicitis; SM, squamous metaplasia

 

Histopathology report

Total

CIN1

CIN2

CIN3

ICC

CC

SM

Lesion -Size

0-5  mm

73

0

0

0

5

5

83

6-15  mm

119

18

1

4

0

0

142

>15  mm

1

8

31

23

12

0

75

Total

193

26

32

27

17

5

300

CIN indicates Cervical intraepithelial neoplasia; ICC, Invasive cervical cancer; CC, Chronic cervicitis; SM, Squamous metaplasia

Group

Didactic posttest marks (%)

Flipped posttest marks (%)

Difference in marks (mean improvement)

P

<50%

63.2 (9.4)

82.2 (10.8)

19.0

<0.001

≥50%

72.4 (14.9)

84.2 ( 10.3)

11.8

<0.001

Data presented as mean (standard deviation)

 Discussion

Despite its potential, robotic rehabilitation adoption in LMICs faces financial, infrastructural, workforce, cultural, and ethical barriers, requiring equitable, sustainable implementation strategies.

Financial and cost barriers

Robotic rehabilitation devices are capital-intensive, often costing between USD 100,000 and 300,000 for a single system. For resource-limited health systems, these costs compete with essential investments in acute care, medicines, and human resources [39]. Maintenance and servicing of devices add recurring expenses, while lack of local manufacturing inflates costs due to import taxes and logistics [40]. Minimal insurance coverage for rehabilitation in LMICs shifts costs to patients and families, disproportionately limiting access to advanced technologies to wealthier groups.

Infrastructure and technical challenges

Robotic rehabilitation requires stable electricity, technical expertise, and suitable infrastructure, which many LMIC facilities lack due to power, connectivity, and space constraints [41].

Workforce and training limitations

LMICs face severe shortages of rehabilitation professionals, with less than 10% of required workforce density compared to HICs [42]. Robotic rehabilitation demands additional training in device operation and safety, necessitating structured education, academic partnerships, and hands-on fellowship programs to prevent underutilization.

Cultural acceptance and patient perspectives

Cultural acceptance of robotics varies; enthusiasm for technology contrasts with distrust of machines, while gender norms may restrict women’s participation, underscoring the need for awareness campaigns and family-centered counselling [43].

Policy and governance gaps

Rehabilitation is often neglected in LMIC health policies, with funding skewed toward acute care and infectious disease management [44]. Robotic rehabilitation requires long-term vision, national policy support, and integration into universal health coverage schemes. Without policy frameworks, centres may remain isolated pilot projects without scalability or sustainability.

Ethical considerations

Robotic rehabilitation raises important ethical issues: Equity: Risk of widening disparities if advanced technologies are limited to affluent patients. Consent and Autonomy: As a new device for human use the patients must understand the risks, limitations, and alternatives before consenting to robotic therapy. · Data Privacy: Devices generate sensitive health data, which require secure storage and protection against misuse. · Prioritization of Resources: Ethical dilemmas arise when scarce funds are spent on robotics while basic rehabilitation services remain underfunded.

Sustainability concerns

Sustainability in LMICs requires management funds, local capacity building, and supply chain resilience. Public–private partnerships, philanthropic support, and domestic innovation may help reduce dependency on imported technology. Local universities and engineering institutions can collaborate with medical centres to design low-cost robotic prototypes adapted to regional needs [45].

Research gaps

Most clinical trials on robotics are conducted in HICs, raising concerns about external validity. LMIC-specific research is sparse, particularly regarding cost-effectiveness, patient satisfaction, and long-term functional outcomes [46]. Without locally generated data, policymakers and funders remain hesitant to scale up robotic rehabilitation.

Conclusion

Robotic rehabilitation improves motor recovery and independence, but its adoption in LMICs is limited by cost, infrastructure, and workforce constraints. The BMU Robotic Rehabilitation Centre demonstrates how advanced technologies can be integrated into resource-limited settings to strengthen access, equity, research, and capacity building.

Acknowledgements
The robotic equipment was provided as a government-to-government gift, with no involvement of manufacturers in study design, data analysis, interpretation, or manuscript preparation.
Author contributions
Concept and design, or design of the research; or the acquisition, analysis, or interpretation of data: MAS. MIH. Drafting the manuscript or revising it critically for important intellectual content: MIH, FN. Final approval of the version to be published: MAS, MIH, FN, MAKA. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: MAS, MAKA.
Conflict of interest
We do not have any conflict of interest.
Data availability statement
We confirm that the data supporting the findings of the study will be shared upon reasonable request.
Supplementary file
None
    References
    1. Morone G, Paolucci S, Cherubini A, De Angelis D, Venturiero V, Coiro P, Iosa M. Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics. Neuropsychiatr Dis Treat. 2017 May 15;13:1303-1311. doi: https://doi.org/10.2147/NDT.S114102
    2. Mehrholz J, Pohl M, Platz T, Kugler J, Elsner B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2018;9:CD006876. doi: https://doi.org/10.1002/14651858.CD006876.pub5
    3. Calabrò RS, Sorrentino G, Cassio A, Mazzoli D, Andrenelli E, Bizzarini E, Campanini I, Carmignano SM, Cerulli S, Chisari C, Colombo V, Dalise S, Fundarò C, Gazzotti V, Mazzoleni D, Mazzucchelli M, Melegari C, Merlo A, Stampacchia G, Boldrini P, Mazzoleni S, Posteraro F, Benanti P, Castelli E, Draicchio F, Falabella V, Galeri S, Gimigliano F, Grigioni M, Mazzon S, Molteni F, Morone G, Petrarca M, Picelli A, Senatore M, Turchetti G, Bonaiuti D; Italian Consensus Conference on Robotics in Neurorehabilitation (CICERONE). Robotic-assisted gait rehabilitation following stroke: a systematic review of current guidelines and practical clinical recommendations. Eur J Phys Rehabil Med. 2021 Jun;57(3):460-471. doi: https://doi.org/10.23736/S1973-9087.21.06887-8
    4. World Health Organization. World report on disability. Geneva: WHO; 2011. Available at: URL: https://www.who.int/publications/i/item/9789241564182. [Accessed on 15 Dec 2025]
    5. Veerbeek JM, Langbroek-Amersfoort AC, Van Wegen EE, Meskers CG, Kwakkel G. Effects of robot-assisted therapy for the upper limb after stroke: a systematic review and meta-analysis. Neurorehabil Neural Repair. 2017;31(2):107–21. doi: https://doi.org/10.1177/1545968316666957
    6. Molteni F, Gasperini G, Cannaviello G, Guanziroli E. Exoskeleton and end-effector robots for upper and lower limbs rehabilitation: narrative review. PM R. 2018;10(9 Suppl 2): S174–88. doi: https://doi.org/10.1016/j.pmrj.2018.06.005
    7. Lo AC, Guarino PD, Richards LG, Haselkorn JK, Wittenberg GF, Federman DG, Ringer RJ, Wagner TH, Krebs HI, Volpe BT, Bever CT Jr, Bravata DM, Duncan PW, Corn BH, Maffucci AD, Nadeau SE, Conroy SS, Powell JM, Huang GD, Peduzzi P. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010 May 13;362(19):1772-83. doi: https://doi.org/10.1056/NEJMoa0911341
    8. Bertani R, Melegari C, De Cola MC, Bramanti A, Bramanti P, Calabrò RS. Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis. Neurol Sci. 2017;38(9):1561–9. doi: https://doi.org/10.1007/s10072-017-2995-5
    9. Sale P, Franceschini M, Waldner A, Hesse S. Use of the robot-assisted gait therapy in rehabilitation of patients with stroke and spinal cord injury. Eur J Phys Rehabil Med. 2012;48(1):111–21. PMID: 22543557
    10. Miller LE, Zimmermann AK, Herbert WG. Clinical effectiveness and safety of powered exoskeleton-assisted walking in patients with spinal cord injury: systematic review with meta-analysis. Med Devices (Auckl). 2016 Mar 22;9:455-66. doi: https://doi.org/10.2147/MDER.S103102
    11. Beretta E, Romei M, Molteni E, Avantaggiato P, Strazzer S. Combined robotic-aided gait training and physical therapy improve functional abilities and hip kinematics during gait in children and adolescents with acquired brain injury. Brain injury. 2015 Jul 3;29(7-8):955-62. doi: https://doi.org/10.3109/02699052.2015.1005130
    12. MEDICA EM. Effects of robot-assisted gait training on postural instability in Parkinson’s disease: A systematic review. European journal of physical and rehabilitation medicine. 2021 Apr 7. doi: https://doi.org/10.23736/S1973-9087.21.06939-2
    13. Calabrò RS, Russo M, Naro A, De Luca R, Leo A, Tomasello P, Molonia F, Dattola V, Bramanti A, Bramanti P. Robotic gait training in multiple sclerosis rehabilitation: Can virtual reality make the difference? Findings from a randomized controlled trial. J Neurol Sci. 2017 Jun 15;377:25-30. doi: https://doi.org/10.1016/j.jns.2017.03.047
    14. Lerner ZF, Damiano DL, Park HS, Gravunder AJ, Bulea TC. A robotic exoskeleton for treatment of crouch gait in children with cerebral palsy: design and initial application. IEEE Trans Neural Syst Rehabil Eng. 2017;25(6):650–9. doi: https://doi.org/10.1109/TNSRE.2016.2595501
    15. Payedimarri AB, Ratti M, Rescinito R, Vanhaecht K, Panella M. Effectiveness of platform-based robot-assisted rehabilitation for musculoskeletal or neurologic injuries: a systematic review. Bioengineering. 2022 Mar 22;9(4):129. doi: https://doi.org/10.3390/bioengineering9040129
    16. Kiyono K, Tanabe S, Hirano S, Ii T, Nakagawa Y, Tan K, Saitoh E, Otaka Y. Effectiveness of Robotic Devices for Medical Rehabilitation: An Umbrella Review. J Clin Med. 2024 Nov 4;13(21):6616. doi: https://doi.org/10.3390/jcm13216616
    17. Moulaei K, Bahaadinbeigy K, Haghdoost AA, Nezhad MS, Sheikhtaheri A. Overview of the role of robots in upper limb disabilities rehabilitation: a scoping review. Arch Public Health. 2023;81(1):84. doi: https://doi.org/10.1186/s13690-023-01100-8
    18. Cieza A, Causey K, Kamenov K, Hanson SW, Chatterji S, Vos T. Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: a systematic analysis for the World Health Organization. Lancet. 2020;396(10267):2006–17. doi: https://doi.org/10.1016/S0140-6736(20)32340-0
    19. Jesus TS, Landry MD, Dussault G, Fronteira I. Human resources for health (and rehabilitation): Six Rehab-Workforce Challenges for the century. Hum Resour Health. 2017;15(1):8. doi: https://doi.org/10.1186/s12960-017-0182-7
    20. Gimigliano F, Negrini S. The World Health Organization. European Journal Of Physical and Rehabilitation Medicine. 2017 Apr 1;53(2):155-68. doi: https://doi.org/10.23736/S1973-9087.17.04746-3
    21. Gutenbrunner C, Bickenbach J, Melvin J, Lains J, Nugraha B. Strengthening health-related rehabilitation services at the national level. Journal of Rehabilitation Medicine. 2018;50(4):317-25. doi: https://doi.org/10.2340/16501977-2217
    22. Uddin T, Islam MT, Rathore FA, O'Connell C. Disability and rehabilitation medicine in Bangladesh: current scenario and future perspectives. Journal of the International Society of Physical and Rehabilitation Medicine. 2019 Oct 1;2(4):168-77. doi: https://doi.org/10.4103/jisprm.jisprm_61_19
    23. Negrini S, Grabljevec K, Boldrini P, Kiekens C, Moslavac S, Zampolini M, Christodoulou N. Up to 2.2 million people experiencing disability suffer collateral damage each day of COVID-19 lockdown in Europe. European journal of physical and rehabilitation medicine. 2020;56(3). doi: https://doi.org/10.23736/s1973-9087.20.06361-3
    24. Mitter N, Ali A, Scior K. Stigma experienced by families of individuals with intellectual disabilities and autism: A systematic review. Research in developmental disabilities. 2019 Jun 1; 89:10-21. doi: https://doi.org/10.1016/j.ridd.2019.03.001
    25. Talukdar JR, Mahmud I, Rashid SF. Primary health care seeking behaviour of people with physical disabilities in Bangladesh: a cross-sectional study. Archives of Public Health. 2018 Sep 3;76(1):43. doi: https://doi.org/10.1186/s13690-018-0293-1
    26. Maciejasz P, Eschweiler J, Gerlach-Hahn K, Jansen-Troy A, Leonhardt S. A survey on robotic devices for upper limb rehabilitation. Journal of neuroengineering and rehabilitation. 2014 Jan 9;11(1):3. Available at: http://www.jneuroengrehab.com/content/11/1/3. [Accessed on 15 Dec 2025]
    27. Turolla A, Rossettini G, Viceconti A, Palese A, Geri T. Musculoskeletal physical therapy during the COVID-19 pandemic: is telerehabilitation the answer? Phys Ther. 2020;100(8):1260–4. doi: https://doi.org/10.1093/ptj/pzaa093
    28. Aziz M, Bipasha N, Gupta U, Ramnarine IV, Redgrave J, Ali AN, Majid A, Bell SM. Stroke in Bangladesh: A narrative review of epidemiology, risk factors and acute stroke services. Journal of Cardiovascular Development and Disease. 2025 Feb 5;12(2):58. doi: https://doi.org/10.3390/jcdd12020058
    29. Uddin T, Islam MT, Hossain M, Hossain MS, Salek AK, Islam MJ, Haque S, Rahim HR, Hossain MS, Hassanuzzaman M, Islam M. Demographic and Clinical Characteristics of Persons With Spinal Cord Injury in Bangladesh: Database for the International Spinal Cord Injury Community Survey 2023. Neurotrauma Reports. 2023 Sep 1;4(1):598-604. doi: https://doi.org/10.1089/neur.2023.0040
    30. Ahmed SM, Emran M, Hasan MI, Newaz F, Ahmed B, Khandaker MN, Emran MA. Correlation of pain, physical function and radiography with osteoarthritis of the knee. KYAMC Journal. 2020 Mar 1;10(4):173-8. doi: https://doi.org/10.3329/kyamcj.v10i4.45714
    31. BSPMR - Bangladesh Society of Physical Medicine & Rehabilitation [Internet]. Bspmr.org. 2025 [cited 2025 Oct 2]. Available at: https://www.bspmr.org/ [Accessed on 15 Dec 2025]
    32. Rahman MS. Community-based rehabilitation in Bangladesh, health components need to be integrated with primary health care. Journal of Enam Medical College. 2018 Feb 7;8(1):41-5. Available at: https://banglajol.info/index.php/JEMC/article/view/35435. [Accessed on 15 Dec 2025]
    33. Rahman MM, Islam MR, Rahman MS, Hossain F, Alam A, Rahman MO, Jung J, Akter S. Forgone healthcare and financial burden due to out-of-pocket payments in Bangladesh: a multilevel analysis. Health economics review. 2022 Jan 10;12(1):5. doi: https://doi.org/10.1186/s13561-021-00348-6
    34. Saha S, Reza SB, Mazid MA, Dewan SM. The Urgent Need for Developing a Common Health Insurance Policy in Bangladesh: A Perspective. Health Science Reports. 2025 Mar;8(3):e70554. doi: https://doi.org/10.1002/hsr2.70554
    35. Koly KN, Abdullah R, Shammi FA, Akter T, Hasan MT, Eaton J, Ryan GK. Mental health and community-based rehabilitation: a qualitative description of the experiences and perspectives of service users and carers in Bangladesh. Community mental health journal. 2022 Jan;58(1):52-66. Available at: https://link.springer.com/article/10.1007/s10597-021-00790-0. [Accessed on 15 Dec 2025]
    36. Bright T, Wallace S, Kuper H. A systematic review of access to rehabilitation for people with disabilities in low-and middle-income countries. International journal of environmental research and public health. 2018 Oct;15(10):2165. doi: https://doi.org/10.3390/ijerph15102165
    37. Nuri RP, Aldersey HM, Ghahari S, Huque AS, Shabnam J. The Bangladeshi rights and protection of persons with disability act of 2013: A policy analysis. Journal of Disability Policy Studies. 2022 Dec;33(3):178-87. doi: https://doi.org/10.1177/10442073211066789
    38. Uddin M, Ganapathy K, Syed-Abdul S. Digital Technology Enablers of Tele-Neurorehabilitation in Pre-and Post-COVID-19 Pandemic Era–A Scoping Review. International Journal of Telerehabilitation. 2024 Jun 28;16(1): e6611. doi: https://doi.org/10.5195/ijt.2024.6611
    39. Louie DR, Eng JJ. Powered robotic exoskeletons in post-stroke rehabilitation of gait: a scoping review. Journal of neuroengineering and rehabilitation. 2016 Jun 8;13(1):53. doi: https://doi.org/10.1186/s12984-016-0162-5
    40. Chang WH, Kim YH. Robot-assisted gait training in stroke rehabilitation. J Stroke. 2013;15(3):174–81. doi: https://doi.org/10.5853/jos.2013.15.3.174
    41. Frontera WR, Stucki G, Engkasan JP, Francisco GE, Gutenbrunner C, Hasnan N, Lains J, Yusof YM, Negrini S, Omar Z, Battistella LR. Advancing academic capacity in physical and rehabilitation medicine to strengthen rehabilitation in health systems worldwide: a joint effort by the European academy of rehabilitation medicine, the association of academic physiatrists, and the international society of physical and rehabilitation medicine. Journal of the International Society of Physical and Rehabilitation Medicine. 2022 Jul 1;5(3):105-13. doi: https://doi.org/10.4103/ijprm.JISPRM-000168
    42. Jesus TS, Landry MD, Hoenig H. Global need for physical rehabilitation: systematic analysis from the Global Burden of Disease Study 2017. Int J Environ Res Public Health. 2019;16(6):980. doi: https://doi.org/10.3390/ijerph16060980
    43. Mutongi C, Rigava B. Robotics in Healthcare: The African Perspective. In2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) 2024 Feb 7 (pp. 1-10). IEEE. doi: https://doi.org/10.1109/ICAIC60265.2024.10433838
    44. Neill R, Shawar YR, Ashraf L, Das P, Champagne SN, Kautsar H, Zia N, Michlig GJ, Bachani AM. Prioritizing rehabilitation in low-and middle-income country national health systems: a qualitative thematic synthesis and development of a policy framework. International Journal for Equity in Health. 2023 May 17;22(1):91. doi: https://doi.org/10.1186/s12939-023-01896-5
    45. Slucock T. A systematic review of low-cost actuator implementations for lower-limb exoskeletons: A technical and financial perspective. Journal of intelligent & robotic systems. 2022 Sep;106(1):3. doi: https://doi.org/10.1007/s10846-022-01695-0
    46. Prvu Bettger J, Resnik LJ. Telerehabilitation in the age of COVID-19: an opportunity for learning health system research. Phys Ther. 2020;100(11):1913–1916. doi: https://doi.org/10.1093/ptj/pzaa151