Evidence map›Paper›PMID 42374045›Full record

ArticleScientific data2026

An IMU and sEMG dataset of induced stumble events during treadmill walking in transfemoral amputees.

L Galey, G Beckmann, R V Gonzalez

Abstract readDataset
In one paragraph

Article in Scientific data, 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

3 authors.

L GaleyDepartment of Engineering Education & Leadership, The University of Texas at El Paso, El Paso, TX, USA. ljgaley2@utep.edu.
G BeckmannDepartment of Mechanical Engineering, The University of Texas at El Paso, El Paso, TX, USA.
R V GonzalezDepartment of Engineering Education & Leadership, The University of Texas at El Paso, El Paso, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Falls and stumble-related events remain a safety concern for individuals with unilateral transfemoral amputation, including experienced users of microprocessor-controlled prosthetic knees. Although wearable inertial and myoelectric sensors are commonly used to study gait perturbations and recovery strategies, datasets capturing experimentally induced stumble events in this population are limited. This article describes a multimodal dataset of treadmill walking and induced stumble responses collected from five individuals with unilateral transfemoral amputation across three prosthetic knee conditions. Wearable inertial measurement units (IMUs) mounted on the prosthetic thigh and shank recorded tri-axial acceleration and angular velocity at 60 Hz, while surface electromyography (sEMG) from the semitendinosus, biceps femoris, and rectus femoris was recorded at 480 Hz. Stumble events were induced using three controlled mechanisms, obstacle contact, bungee-induced braking, and uneven terrain, and marked in real time with embedded timestamps. Each event was subsequently reviewed using synchronized video and classified according to the observed gait outcome as unaffected gait, affected gait, recovery, or fall. The dataset consists of raw, fully time-aligned IMU and sEMG recordings with embedded stumble markers, providing data for analysis of gait disruption and recovery behavior and for sensor-based analyses of stumble events and prosthetic knee control in individuals with unilateral transfemoral amputation.

Indexed as

AmputeesElectromyographyWalkingAccidental FallsArtificial LimbsGaitHumansWearable Electronic Devices

Identifiers

PMID42374045
PMCPMC13683326

What OpenQuestion holds

Textmetadata
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.