Evidence map›Paper›PMID 40363331›Full record

ReviewSensors (Basel, Switzerland)2025

From Sensors to Care: How Robotic Skin Is Transforming Modern Healthcare-A Mini Review.

Yuting Zhu, Wendy Moyle, Min Hong, Kean Aw

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. 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

4 authors.

Yuting ZhuSchool of Engineering, University of Southern Queensland, Springfield, QLD 4300, Australia.ORCID 0000-0002-4955-2733
Wendy MoyleSchool of Nursing and Midwifery, Griffith University, Nathan, QLD 4111, Australia.ORCID 0000-0003-3004-9019
Min HongSchool of Engineering, University of Southern Queensland, Springfield, QLD 4300, Australia.
Kean AwDepartment of Mechanical and Mechatronics Engineering, University of Auckland, Auckland 1010, New Zealand.ORCID 0000-0001-9308-508X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, robotics has made notable progress, becoming an essential component of daily life by facilitating complex tasks and enhancing human experiences. While most robots have traditionally featured hard surfaces, the growing demand for more comfortable and safer human-robot interactions has driven the development of soft robots. One type of soft robot, which incorporates innovative skin materials, transforms rigid structures into more pliable and adaptive forms, making them better suited for interacting with humans. Especially in healthcare and rehabilitation, robotic skin technology has gained substantial attention, offering transformative solutions for improving the functionality of prosthetics, exoskeletons, and companion robots. Although replicating the complex sensory functions of human skin remains a challenge, ongoing research in soft robotics focuses on developing sensors that mimic the softness and tactile sensitivity necessary for effective interaction. This review provides a narrative analysis of current trends in robotic skin development, specifically tailored for healthcare and rehabilitation applications, including skin types of sensor technologies, materials, challenges, and future research directions in this rapidly developing field.

Indexed as

Biosensing TechniquesDelivery of Health CareRoboticsSkinHumansTouchWearable Electronic Devicescompanion robotshealthcarehealthcare robotsrehabilitationrobotic skintactile sensing

Identifiers

PMID40363331
PMCPMC12074484

What Socratic 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 Socratic graph.