Evidence map›Paper›PMID 38595947›Full record

ArticleMicrosystems & nanoengineering2024

A laser-engraved wearable gait recognition sensor system for exoskeleton robots.

Maowen Sun, Songya Cui, Zezheng Wang, Huayu Luo, Huayong Yang, Xiaoping Ouyang, Kaichen Xu

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Geometric Feature Relationship-Based Knowledge Distillation for Ground Reaction Force Estimation.Measurement : journal of the International Measurement Confederation · 2026
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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.

Maowen Sun *State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
Songya Cui *School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, 310015 China.
Zezheng WangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
Huayu LuoState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
Huayong YangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
Xiaoping OuyangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
Kaichen XuState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.ORCID 0000-0003-4957-3144

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a reinforcement technology that improves load-bearing ability and prevents injuries, assisted exoskeleton robots have extensive applications in freight transport and health care. The perception of gait information by such robots is vital for their control. This information is the basis for motion planning in assistive and collaborative functions. Here, a wearable gait recognition sensor system for exoskeleton robots is presented. Pressure sensor arrays based on laser-induced graphene are developed with flexibility and reliability. Multiple sensor units are integrated into an insole to detect real-time pressure at key plantar positions. In addition, the circuit hardware and the algorithm are designed to reinforce the sensor system with the capability of gait recognition. The experimental results show that the accuracy of gait recognition by the proposed system is 99.85%, and the effectiveness of the system is further verified through testing on an exoskeleton robot.

Indexed as

Electrical and electronic engineeringSensors

Identifiers

PMID38595947
PMCPMC11002036

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Registered trials

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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.