Evidence map›Paper›PMID 37754176›Full record

ArticleBiomimetics (Basel, Switzerland)2023

Lightweight Soft Robotic Glove with Whole-Hand Finger Motion Tracking for Hand Rehabilitation in Virtual Reality.

Fengguan Li, Jiahong Chen, Zhitao Zhou, Jiefeng Xie, Zishu Gao, Yuxiang Xiao, Pei Dai, Chanchan Xu, Xiaojie Wang, Yitong Zhou

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Real-Time Home Automation System Using BCI Technology.Biomimetics (Basel, Switzerland) · 2024
    Article
  6. Article
  7. Article
  8. 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

10 authors.

Fengguan LiShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China.ORCID 0000-0001-5643-3110
Jiahong ChenShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China.ORCID 0000-0001-7188-2467
Zhitao ZhouShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China.
Jiefeng XieShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China.
Zishu GaoShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China.ORCID 0009-0001-8241-5562
Yuxiang XiaoShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China.
Pei DaiShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China.
Chanchan XuThe Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Changzhou 213164, China.
Xiaojie WangThe Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Changzhou 213164, China.ORCID 0000-0002-4740-7882
Yitong ZhouShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 510641, China.

Funding

Guangzhou Municipal Science and Technology Project 202201010179National Natural Science Foundation of China 62203174
6 · The paper itself

Abstract

Soft robotic gloves have attracted significant interest in hand rehabilitation in the past decade. However, current solutions are still heavy and lack finger-state monitoring and versatile treatment options. To address this, we present a lightweight soft robotic glove actuated by twisted string actuators (TSA) that provides whole-hand finger motion tracking. We have developed a virtual reality environment for hand rehabilitation training, allowing users to interact with various virtual objects. Fifteen small inertial measurement units are placed on the glove to predict finger joint angles and track whole-hand finger motion. We performed TSA experiments to identify design and control rules, by understanding how their response varies with input load and voltages. Grasping experiments were conducted to determine the grasping force and range of motion. Finally, we showcase an application of the rehabilitation glove in a Unity-based VR interface, which can actuate the operator's fingers to grasp different virtual objects.

Indexed as

finger motion trackinghand rehabilitationsoft robotic glovetwisted string actuatorvirtual reality

Identifiers

PMID37754176
PMCPMC10526423

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.