Evidence map›Paper›PMID 38409128›Full record

ReviewNature communications2024

Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement.

Haisheng Xia, Yuchong Zhang, Nona Rajabi, Farzaneh Taleb, Qunting Yang, Danica Kragic, Zhijun Li

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
16.6field-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

22 citing papers in PubMed, 90 citations in OpenAlex.

  1. Review
  2. A neuromorphic robotic electronic skin with active pain and injury perception.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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 at 3 institutions in 3 countries.

Haisheng XiaSchool of Mechanical Engineering, Tongji University, Shanghai, 201804, China.ORCID 0000-0001-8574-6650
Yuchong ZhangRobotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden.
Nona RajabiRobotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden.
Farzaneh TalebRobotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden.
Qunting YangDepartment of Automation, University of Science and Technology of China, Hefei, 230026, China.
Danica KragicRobotics, Perception and Learning Lab, EECS at KTH Royal Institute of Technology Stockholm, 114 17, Stockholm, Sweden.
Zhijun LiSchool of Mechanical Engineering, Tongji University, Shanghai, 201804, China. zjli@ieee.org.ORCID 0000-0002-3909-488X
KTH Royal Institute of Technology · SETongji University · CNUniversity of Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most wearable robots such as exoskeletons and prostheses can operate with dexterity, while wearers do not perceive them as part of their bodies. In this perspective, we contend that integrating environmental, physiological, and physical information through multi-modal fusion, incorporating human-in-the-loop control, utilizing neuromuscular interface, employing flexible electronics, and acquiring and processing human-robot information with biomechatronic chips, should all be leveraged towards building the next generation of wearable robots. These technologies could improve the embodiment of wearable robots. With optimizations in mechanical structure and clinical training, the next generation of wearable robots should better facilitate human motor and sensory reconstruction and enhancement.

Indexed as

Exoskeleton DeviceRoboticsWearable Electronic DevicesElectronicsHumansTechnology

Identifiers

PMID38409128
PMCPMC10897332
OpenAlexW4392169153

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.