Evidence map›Paper›PMID 41796186›Full record

ArticleScientific reports2026

KNEESENSE: a low-cost wearable system with hydraulic filament sensing for real-time knee rehabilitation monitoring.

Nhu An Phan, Sy Trung Ngo, Minh Tri Phan, Nghiep Quy Duong, Phuoc Thien Phan, Thanh Nho Do, Phuong Cao, Girish Krishnan, Elizabeth T Hsiao-Wecksler, Mai Thanh Thai

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Article in Scientific reports, 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

10 authors.

Nhu An Phan *College of Engineering and Computer Science, VinUniversity, Hanoi, 100000, Vietnam.
Sy Trung Ngo *College of Engineering and Computer Science, VinUniversity, Hanoi, 100000, Vietnam.
Minh Tri PhanCollege of Engineering and Computer Science, VinUniversity, Hanoi, 100000, Vietnam.
Nghiep Quy DuongCollege of Engineering and Computer Science, VinUniversity, Hanoi, 100000, Vietnam.
Phuoc Thien PhanFaculty of Engineering, Graduate School of Biomedical Engineering, UNSW Sydney, Kensington Campus, Sydney, NSW, 2052, Australia.
Thanh Nho DoFaculty of Engineering, Graduate School of Biomedical Engineering, UNSW Sydney, Kensington Campus, Sydney, NSW, 2052, Australia.
Phuong CaoNational Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Girish KrishnanDepartment of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Elizabeth T Hsiao-WeckslerDepartment of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
Mai Thanh ThaiCollege of Engineering and Computer Science, VinUniversity, Hanoi, 100000, Vietnam. thanh.tm2@vinuni.edu.vn.

Funding

VinUni Fast Track Grant 400186VinUni-Illinois Smart Health Center (VISHC) Project 13
6 · The paper itself

Abstract

Knee injuries, if not properly rehabilitated, can lead to long-term gait impairment. However, access to rehabilitation is often limited by cost, travel, and adherence challenges, especially in developing countries. Despite advances in soft sensor technologies, current home-based rehabilitation systems often lack the affordability, portability, and usability needed for broad adoption. This study presents a novel, low-cost, and fully wearable wireless system designed to monitor knee joint angles during rehabilitation with high precision and user convenience. At the core of the system is a custom-designed wearable hydraulic filament sensor (WHFS), costing just $5, integrated into a knee pad and paired with a reusable $20 wireless data acquisition (DAQ) unit. The DAQ transmits real-time joint angle data to a companion mobile app, featuring automatic calibration for consistent multi-user performance. The WHFS distinguishes itself through its optimized hydraulic sensing mechanism, offering a minimum detectable displacement of 0.2 mm, a sensitivity of 2.65, and maximum hysteresis of 4.6%. Testing shows robust performance with an RMSE of 4.1° in angle estimation and just 0.57% signal degradation after 100 full-range flexion cycles. By combining affordability, portability, and precision, this system addresses key barriers in home-based rehabilitation and offers a scalable, noninvasive solution for improving recovery outcomes.

Indexed as

Knee InjuriesKnee JointWearable Electronic DevicesHumansMonitoring, PhysiologicWireless TechnologyAssistive devicesHome-based rehabilitationKnee rehabilitationSoft roboticsSoft sensors

Identifiers

PMID41796186
PMCPMC13087177

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.