Evidence map›Paper›PMID 40486669›Full record

ArticleJapanese journal of comprehensive rehabilitation science2025

Real-Time Estimation of Lower Limb Posture and Joint Angles Using Wearable IMUs: Reproduced Hemiparetic and Normal Gait.

Shun Kanega, Yoshihiro Muraoka

Abstract read
In one paragraph

Article in Japanese journal of comprehensive rehabilitation science, 2025. 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Shun KanegaFaculty of Human Sciences, Waseda University, Tokorozawa, Saitama, Japan.
Yoshihiro MuraokaFaculty of Human Sciences, Waseda University, Tokorozawa, Saitama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kanega S, Muraoka Y. Real-Time Estimation of Lower Limb Posture and Joint Angles Using Wearable IMUs: Reproduced Hemiparetic and Normal Gait. Jpn J Compr Rehabil Sci 2025; 16: 19-29. Objective: A low-computational-load posture estimation method was employed, assuming integration into a functional electrical stimulation (FES) device, to estimate thigh and shank inclination angles as well as the knee joint angle during gait. The accuracy and processing time of the method were evaluated to assess its practical feasibility. Methods: Two inertial measurement units (IMUs) were mounted on the lateral thigh and shank. Two healthy adults performed level-ground gait, including normal gait and gait reproducing the characteristics of circumduction gait, which is typically observed in individuals with post-stroke hemiparesis. A custom gait pacemaker was developed to replicate the spatiotemporal asymmetry of hemiparetic gait. Posture estimation in the sagittal and frontal planes was performed on a microcontroller using a low-computational-load Madgwick filter, and the processing time was recorded. The estimation accuracy was assessed by comparing the results with optical motion capture data using root mean square error (RMSE) and cross-correlation analysis. Results: The average total processing time, including sampling, posture estimation, and stimulation control, was 6.8 ms. The RMSE of sagittal plane posture angles was less than 4° in all cases, suggesting estimation accuracy comparable to previous studies. For the frontal plane posture angles, the estimation accuracy was relatively high during the reproduced circumduction gait, indicating that the system effectively captured the circumduction motion. Conversely, the accuracy tended to be lower during normal gait. Conclusion: Compared with previous studies, the posture estimation method used in this study, which employed the Madgwick filter, was able to estimate posture without compromising accuracy. The entire process, including sampling, posture estimation, and stimulation control, was completed within 10 ms, reflecting the feasibility of real-time processing at 100 Hz.

Indexed as

6-axis IMUcircumduction gaitknee joint angleshank inclination anglethigh inclination angle

Identifiers

PMID40486669
PMCPMC12140857

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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.