Evidence map›Paper›PMID 42540021›Full record

ArticleJournal of rehabilitation and assistive technologies engineering

Design and preliminary bench-level validation of a low-cost 3D-printed hip-knee exoskeleton for lower-limb rehabilitation training.

Md Fazle Rabbi, Helal An Nahiyan, Sourav Karmaker, Fahim Islam Anik

Abstract read
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Article in Journal of rehabilitation and assistive technologies engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Md Fazle RabbiDepartment of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh.
Helal An NahiyanDepartment of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh.
Sourav KarmakerDepartment of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh.ORCID https://orcid.org/0009-0008-6394-8452
Fahim Islam AnikDepartment of Mechanical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lower-limb impairment caused by neurological injury, musculoskeletal disorders, or reduced mobility often requires repetitive rehabilitation exercises, but continuous therapist-assisted therapy may be difficult to access in low-resource settings. In this study, we developed and preliminarily evaluated a low-cost, lightweight, portable hip-knee exoskeleton prototype capable of generating controlled hip and knee joint motion for rehabilitation-oriented range-of-motion training. The prototype was constructed mainly of 3D-printed polylactic acid parts and powered by a pair of high-torque servo motors in the hip and knee joints. A Bluetooth-connected Android app was programmed to offer two modes: rehabilitation mode for independent joint control and a gait-inspired sinusoidal mode that generates coordinated hip and knee joint trajectories. The cost of the materials used to construct the prototype is estimated to be USD 155 with a weight of about 2.1 kg. Joint motion performance was assessed through time-angle measurements at three predefined speed settings. The hip joint (approximately 60° movement) was achieved in 2.2, 1.6, and 1.1 s and the knee joint (approximately 140° movement) was achieved in 3.0, 2.0, and 0.6 s for slow, medium and fast modes, respectively. The trial-to-trial variation in commanded servo position remained below ±1° under the tested conditions. The results demonstrate the feasibility of generating repeatable commanded hip and knee joint motion under controlled laboratory conditions. However, further investigations involving quantitative load-bearing evaluation, safety assessment, user studies, and clinical trials are required before the system can be considered for practical rehabilitation applications.

Indexed as

3D printingassistive technologybench-level validationhip–knee exoskeletonlower-limb rehabilitationmobile-app controlrehabilitation engineeringwearable robotics

Identifiers

PMID42540021
PMCPMC13424928

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.