Evidence map›Paper›PMID 41817006›Full record

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

AI-Assisted Self-Powered Wearable Dual-Mode Sensor With TENG and Stretchable Optical Fiber for Neurological Disorder Diagnostics.

Tianliang Li, Han Liu, Guoxu Liu, Qian'ao Wang, Haotian Zhou, Guiyi Liu, Yan Xu, Jun Wang, Zuqiang Wang, Feng Zhu and 3 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Tianliang LiSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0000-0002-7323-169X
Han LiuSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, China.
Guoxu LiuBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, P. R. China.
Qian'ao WangSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0009-0003-7340-871X
Haotian ZhouSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, China.
Guiyi LiuSchool of Information Engineering, Wuhan University of Technology, Wuhan, Hubei, China.
Yan XuDepartment of Neurology, Union Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Jun WangRenmin Hospital of Wuhan University, Wuhan, Hubei, China.
Zuqiang WangDepartment of Encephalopathy II, Yangxin County Traditional Chinese Medicine Hospital, Huangshi, Hubei, China.
Feng ZhuDepartment of Neurology, Yangxin County People's Hospital, Huangshi, Hubei, China.
Feiling LuoBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, P. R. China.
Zhong Lin WangBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, P. R. China.
Chi ZhangBeijing Key Laboratory of High-Entropy Energy Materials and Devices, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, P. R. China.ORCID https://orcid.org/0000-0002-7511-805X

Funding

Beijing Natural Science Foundation L247020Key Research and Development Program of Hubei Province 2024BCB056National Natural Science Foundation of China 52275541National Natural Science Foundation of China 52450006National Natural Science Foundation of China U23A20640
6 · The paper itself

Abstract

Wearable sensors hold significant potential for managing lower-limb dysfunction in neurological disorders, but current systems remain constrained by unimodal sensing, external power dependence, and limited diagnostic capabilities. Here, we present a wireless wearable dual-mode sensor (WDMS) integrating three polyurethane-based flexible optical strain (PFOS) components with a contact-separation mode triboelectric nanogenerator (CS-TENG). The PFOS components are used for muscle signal monitoring, while the CS-TENG simultaneously monitors plantar pressure and harvests biomechanical energy to power the WDMS, eliminating external power dependence. Furthermore, leveraging gait data from 60 individuals, an embedded Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model achieved 94.23% accuracy in distinguishing Parkinson's disease (PD) and stroke, while quantitatively evaluating rehabilitation progress after pharmacological and physical therapy interventions. By synergizing multimodal sensing, AI-driven analysis, and clinical validation, this technology advances beyond passive monitoring to provide intelligent diagnostic support. Its self-sufficiency and scalability facilitate transformative home-based management of neurological disorders.

Indexed as

Artificial IntelligenceNervous System DiseasesOptical FibersParkinson DiseaseStrokeWearable Electronic DevicesHumansIntelligent SystemsAI‐assisted diagnosisdual‐mode sensorself‐poweredtriboelectric nanogenerator

Identifiers

PMID41817006
PMCPMC13170242

What Socratic 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 Socratic graph.