Evidence map›Paper›PMID 42655577›Full record

ArticleSensors (Basel, Switzerland)2026

Flexible Gait Sensing and Machine Learning Recognition Based on Phase-Separated PVDF-HFP Films.

Huimin Liang, Qi Shao, Fuhao Wu, Yibo Xiong, Wenwu Wang, Hongbin Su, Xiyao Huang, Zilu Hu, Yixin Wang, Liang He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Huimin LiangSchool of Mechanical Engineering, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China.
Qi ShaoSchool of Mechanical Engineering, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China.
Fuhao WuSchool of Mechanical Engineering, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China.
Yibo XiongSchool of Mechanical Engineering, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China.
Wenwu WangSchool of Mechanical Engineering, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China.
Hongbin SuSchool of Mechanical Engineering, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China.ORCID 0009-0005-6117-8499
Xiyao HuangSchool of Mechanical Engineering, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China.
Zilu HuSchool of Mechanical Engineering, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China.
Yixin WangSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Liang HeSchool of Mechanical Engineering, State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China.ORCID 0000-0002-7402-9194

Funding

Fundamental Research Funds for the Central Universities 20822041F4045
6 · The paper itself

Abstract

Flexible wearable piezoelectric sensors have attracted increasing attention in human motion monitoring and motion classification applications due to their self-powered sensing capability and rapid response. In this work, poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) flexible piezoelectric films were fabricated using a phase separation method with different loading masses of PVDF-HFP to regulate the crystal structure and output signal characteristics of the films. X-ray diffraction and Fourier-transform infrared spectroscopy analyses demonstrated that an appropriate mass of PVDF-HFP promoted the formation of polar β-phase crystals, and the optimized film exhibited a β-phase content of 86.81%. The prepared films generated stable and distinguishable response signals under different gait conditions, indicating high potential for flexible motion sensing. Furthermore, machine learning-assisted motion classification was preliminarily performed based on the acquired sensing signals, achieving an accuracy above 90%. This work demonstrates the potential of phase-separated PVDF-HFP films for flexible gait sensing and wearable motion recognition applications.

Indexed as

gait recognitionmachine learningpiezoelectric sensorpoly(vinylidene fluoride-co-hexafluoropropylene)

Identifiers

PMID42655577
PMCPMC13517410

What OpenQuestion holds

Textmetadata
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.