Evidence map›Paper›PMID 41698158›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

A Fully Self-Powered Digital Wearable System for the Auxiliary Treatment of Plantar Fasciitis.

Jiacheng Hou, Ying Hong, Shiyuan Liu, Qiqi Pan, Jingyu Zhang, Qingyang Xu, Qiyi Nie, Zhonghe Wang, Liming Xin, Yilong Wang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. A Fully Self-Powered Digital Wearable System for the Auxiliary Treatment of Plantar Fasciitis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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

11 authors.

Jiacheng HouInstitute of Artificial Intelligence, School of Future Technology, Shanghai University, Shanghai, China.
Ying HongDepartment of Medical Ultrasound, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.ORCID https://orcid.org/0000-0001-6944-9146
Shiyuan LiuThrust of Smart Manufacturing, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China.ORCID https://orcid.org/0000-0003-0966-5589
Qiqi PanDepartment of Mechanical and Aerospace Engineering, Hong Kong University of Science and Technology, Hong Kong, China.ORCID https://orcid.org/0000-0003-0468-3585
Jingyu ZhangSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, China.ORCID https://orcid.org/0009-0007-6700-8161
Qingyang XuInstitute of Artificial Intelligence, School of Future Technology, Shanghai University, Shanghai, China.
Qiyi NieInstitute of Artificial Intelligence, School of Future Technology, Shanghai University, Shanghai, China.
Zhonghe WangInstitute of Artificial Intelligence, School of Future Technology, Shanghai University, Shanghai, China.
Liming XinSchool of Computer Engineering and Science, Shanghai University, Shanghai, China.ORCID https://orcid.org/0000-0002-3447-9932
Yilong WangSchool of Astronautics, Harbin Institute of Technology, Harbin, China.ORCID https://orcid.org/0000-0002-0651-5561
Biao WangInstitute of Artificial Intelligence, School of Future Technology, Shanghai University, Shanghai, China.ORCID https://orcid.org/0000-0002-2746-8207

Funding

General Projects of Natural Science Foundation of Shanghai 24ZR1423200National Natural Science Foundation of China 52575108National Natural Science Foundation of China 52575129National Natural Science Foundation of China 62333014
6 · The paper itself

Abstract

Plantar fasciitis severely impairs daily life through persistent pain and limited mobility, whereas conventional treatments often lack real-time monitoring and personalized feedback. This study introduces a fully self-powered digital wearable system (FS-DWS), integrating an arch support auxiliary (ASA) device, a wearable sensing system (WSS), and a machine learning-driven closed-loop visualized feedback system (VFS) to enable real-time plantar pressure monitoring and abnormal gait recognition for the auxiliary treatment of plantar fasciitis. As a system-level engineering achievement, the ASA module integrates elastic support with energy harvesting, alleviating plantar pressure and powering the wearable sensing system without any batteries, with a maximum power density of 41.6 mW/cm

Indexed as

Fasciitis, PlantarGaitWearable Electronic DevicesBiomechanical PhenomenaDigital HealthHumansMachine LearningPressurebiomechanicaldigital wearablesenergy harvestingflexible sensorreal‐time monitoring

Identifiers

PMID41698158
PMCPMC13116193

What OpenQuestion holds

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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.