Evidence map›Paper›PMID 39761022›Full record

ReviewSoft robotics2025

Soft Robotics in Upper Limb Neurorehabilitation and Assistance: Current Clinical Evidence and Recommendations.

Natalie Tanczak, Aaron Yurkewich, Francesco Missiroli, Seng Kwee Wee, Simone Kager, Hyungmin Choi, Kyu-Jin Cho, Hong Kai Yap, Cristina Piazza, Lorenzo Masia and 1 more

Abstract readReview
In one paragraph

Review in Soft robotics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Review
  4. Article
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

11 authors.

Natalie TanczakSingapore-ETH Centre, Future Health Technologies Programme, Singapore, Singapore.
Aaron YurkewichMechatronics Engineering, Ontario Tech University, Oshawa, Canada.
Francesco MissiroliInstitute of Computer Engineering (ZITI), Faculty of Engineering Sciences, Heidelberg University, Heidelberg, Germany.
Seng Kwee WeeClinic for Advanced Rehabilitation Therapeutics (CART), Tan Tock Seng Hospital, Singapore, Singapore.
Simone KagerSingapore-ETH Centre, Future Health Technologies Programme, Singapore, Singapore.
Hyungmin ChoiDepartment of Mechanical Engineering, Soft Robotics Research Centre (SRRC), Seoul National University, Seoul, Republic of Korea.
Kyu-Jin ChoDepartment of Mechanical Engineering, Soft Robotics Research Centre (SRRC), Seoul National University, Seoul, Republic of Korea.
Hong Kai YapRoceso Technologies, Singapore, Singapore.
Cristina PiazzaSchool of Computation, Information and Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany.
Lorenzo MasiaSchool of Computation, Information and Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany.
Olivier LambercySingapore-ETH Centre, Future Health Technologies Programme, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft robotics is gaining interest in rehabilitation applications, bringing new opportunities to offset the loss of upper limb motor function following neurological, neuromuscular, or traumatic injuries. Unlike conventional rigid robotics, the added softness in linkages or joints promises to make rehabilitation robots compliant, which translates into higher levels of safety, comfort, usability, and portability, opening the door for these rehabilitation technologies to be used in daily life. While several reviews documented the different technical implementations of soft rehabilitation robots, it is essential to discuss the growing clinical evidence on the feasibility and effectiveness of using this technology for rehabilitative and assistive purposes, whether softness brings the expected advantages from the perspective of end users, and how we should proceed in the future of this field. In this perspective article, we present recent clinical evidence on how 13 different upper limb devices were used in both controlled (clinical) and uncontrolled (at home) settings in more than 37 clinical studies. From these findings and our own experience, we derive recommendations for future developers and end users regarding the design, application, and evaluation of soft robotics for upper limb rehabilitation and assistance.

Indexed as

Neurological RehabilitationRoboticsUpper ExtremityEquipment DesignHumansclinical evidencemedical devicesneurorehabilitationportablerecommendationssoft roboticswearable

Identifiers

PMID39761022
PMCPMC12178277

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.