Evidence map›Paper›PMID 41886733›Full record

ArticlePloS one2026

Bot fit: A novel approach to assessing lower limb muscular strength.

Byungmun Kang, DaeEun Kim, Changmin Lee, Dongwoo Kim, Hwang-Jae Lee, Dokwan Lee, YoonMyung Kim, Hyung-Gyu Jeon, Kyunghwan Jung

Abstract read
In one paragraph

Article in PloS one, 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

9 authors.

Byungmun KangBiological Cybernetics Lab, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
DaeEun KimBiological Cybernetics Lab, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0003-0863-1694
Changmin LeeBiological Cybernetics Lab, School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
Dongwoo KimBot Fit T/F, New Business Team, Samsung Electronics, Suwon, Republic of Korea.ORCID https://orcid.org/0000-0002-0668-3602
Hwang-Jae LeeBot Fit T/F, New Business Team, Samsung Electronics, Suwon, Republic of Korea.
Dokwan LeeBot Fit T/F, New Business Team, Samsung Electronics, Suwon, Republic of Korea.
YoonMyung KimUniversity College, Yonsei University International Campus, Incheon, Republic of Korea.
Hyung-Gyu JeonSports Rehabilitation Lab, Department of Physical Education, Yonsei University, Seoul, Republic of Korea.
Kyunghwan JungDepartment of Sports, Leisure, and Recreation, Soonchunhyang University, Chungnam, Republic of Korea.ORCID https://orcid.org/0009-0005-5336-6510

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study introduces Bot Fit, an innovative hip-joint exoskeleton designed for the objective assessment of lower limb muscular strength and exercise performance. A total of 25 participants underwent baseline strength assessments using conventional fitness equipment before performing resistance exercises under two controlled conditions: a 10-second test without speed restrictions and a 30-second test at a constant speed. Bot Fit recorded motor signals and performance metrics, capturing key indicators such as repetitions and movement patterns. To analyze the relationship between muscle parameters and exercise performance, we employed correlation analysis, hierarchical cluster analysis (HCA), and regression analysis. The results demonstrated strong associations between lower limb strength and key exercises, including squats, knee-ups, and reverse lunges, particularly in short-duration and constant-speed conditions. HCA successfully clustered participants based on three exercise-related metrics with an accuracy of 0.89, while the regression model achieved a correlation coefficient of 0.86, indicating high statistical power. These findings suggest that Bot Fit provides a reliable, data-driven method for muscular strength assessment, offering valuable insights for optimizing exercise programs, preventing injuries, and enhancing physical performance. The integration of wearable robotic technology into health and fitness monitoring enables personalized exercise interventions and supports evidence-based decision-making in sports science, rehabilitation, and performance enhancement.

Indexed as

Exoskeleton DeviceLower ExtremityMuscle StrengthAdultFemaleHumansMaleResistance TrainingYoung Adult

Identifiers

PMID41886733
PMCPMC13021169

What OpenQuestion 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 OpenQuestion graph.