Evidence map›Paper›PMID 37530475›Full record

ReviewACS nano2023

Sensing in Soft Robotics.

Chidanand Hegde, Jiangtao Su, Joel Ming Rui Tan, Ke He, Xiaodong Chen, Shlomo Magdassi

Open access · hybridAbstract readReview
In one paragraph

Review in ACS nano, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
30.7field-weighted citation impact, top 1% of its field
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

55 citing papers in PubMed, 280 citations in OpenAlex.

  1. Review
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  12. Soft robotic devices for cardiovascular medicine.Nature reviews. Cardiology · 2026
    Review
  13. Article
  14. Article
  15. A Review on Biodegradable Materials of Sustainable Soft Robotics and Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  16. Article
  17. Article
  18. Article
  19. Review
  20. Nature-Inspired Innovation in Electrical Engineering Technologies and Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
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

6 authors at 2 institutions in 2 countries.

Chidanand HegdeSchool of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Jiangtao SuSchool of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Joel Ming Rui TanSchool of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.ORCID 0009-0002-7418-5565
Ke HeSchool of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Xiaodong ChenSchool of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.ORCID 0000-0002-3312-1664
Shlomo MagdassiSingapore-HUJ alliance for Research and Enterprise (SHARE), Campus for Research Excellence and Technological Enterprise (CREATE) Singapore 138602, Singapore.ORCID 0000-0002-6794-0553
Nanyang Technological University · SGHebrew University of Jerusalem · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft robotics is an exciting field of science and technology that enables robots to manipulate objects with human-like dexterity. Soft robots can handle delicate objects with care, access remote areas, and offer realistic feedback on their handling performance. However, increased dexterity and mechanical compliance of soft robots come with the need for accurate control of the position and shape of these robots. Therefore, soft robots must be equipped with sensors for better perception of their surroundings, location, force, temperature, shape, and other stimuli for effective usage. This review highlights recent progress in sensing feedback technologies for soft robotic applications. It begins with an introduction to actuation technologies and material selection in soft robotics, followed by an in-depth exploration of various types of sensors, their integration methods, and the benefits of multimodal sensing, signal processing, and control strategies. A short description of current market leaders in soft robotics is also included in the review to illustrate the growing demands of this technology. By examining the latest advancements in sensing feedback technologies for soft robots, this review aims to highlight the potential of soft robotics and inspire innovation in the field.

Indexed as

actuation mechanismsexosuitflexible/stretchable sensorsindustry leaders in soft grippersmaterials for soft robotsmultimodal sensingprostheticssignal processingsoft robotic controlsoft robots

Identifiers

PMID37530475
PMCPMC10448757
OpenAlexW4385476004

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.