Evidence map›Paper›PMID 40901638›Full record

ArticleFrontiers in robotics and AI2025

Design, development, and validation of a non-backdrivable active ankle-foot orthosis for the TWIN lower-limb exoskeleton.

Raffaele Giannattasio, Nicolò Boccardo, Riccardo Vaccaro, Heeral Bhatt, Stefano Maludrottu, Elena De Momi, Matteo Laffranchi

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2025. 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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

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

7 authors.

Raffaele GiannattasioRehab Technologies Lab, Italian Institute of Technology, Genoa, Italy.
Nicolò BoccardoRehab Technologies Lab, Italian Institute of Technology, Genoa, Italy.
Riccardo VaccaroRehab Technologies Lab, Italian Institute of Technology, Genoa, Italy.
Heeral BhattRehab Technologies Lab, Italian Institute of Technology, Genoa, Italy.
Stefano MaludrottuRehab Technologies Lab, Italian Institute of Technology, Genoa, Italy.
Elena De MomiDepartment of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Matteo LaffranchiRehab Technologies Lab, Italian Institute of Technology, Genoa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study's primary objective was to develop an Active Ankle-Foot Orthosis (AAFO) specifically designed for integration into lower-limb exoskeletons. An analysis of human ankle motion is conducted to inform the development process, guiding the creation of an AAFO that aligns with specifics extrapolated by real data. The AAFO incorporates an electric motor with a non-backdrivable transmission system, engineered to reduce distal mass, minimize power consumption, and enable high-precision position control. Capable of generating up to 50 Nm of peak torque, the AAFO is designed to provide support throughout the walking cycle, targeting pathological conditions such as foot drop and toe drag. Performance was first validated through benchtop experiments under unloaded conditions. The AAFO was then integrated into the TWIN lower-limb exoskeleton, employing an optimal trajectory planning method to generate compatible reference trajectories. These trajectories are designed to help the user maintain ground contact during the support phase while ensuring safe toe clearance and minimizing jerk during the swing phase. Finally, the AAFO's performance was assessed in real-world application conditions, with four healthy participants walking with the TWIN lower limb exoskeleton. The results suggest that the proposed AAFO efficiently reduces toe clearance, ensures stable control, and maintains low power consumption, highlighting its suitability for clinical applications.

Indexed as

ankle-foot orthosiscontrolexoskeletonminimum jerkmotion planningmultiple sclerosisrehabilitationrobotics

Identifiers

PMID40901638
PMCPMC12399533

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LicenceCC BY
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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.