Evidence map›Paper›PMID 40942656›Full record

ArticleSensors (Basel, Switzerland)2025

Electromyographic and Kinematic Analysis of the Upper Limb During Drinking and Eating Using a Wearable Device Prototype.

Patrícia Santos, Filipa Marquês, Carla Quintão, Cláudia Quaresma

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

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

4 authors.

Patrícia SantosLaboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys), NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal.ORCID 0000-0002-3569-2495
Filipa MarquêsPhysics Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal.ORCID 0009-0007-1598-7193
Carla QuintãoLaboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys), NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal.ORCID 0000-0003-1015-4655
Cláudia QuaresmaLaboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys), NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal.ORCID 0000-0001-9978-261X

Funding

Fundação para a Ciência e Tecnologia [UI/BD/151321/2021] and [LA/P/0117/2020]
6 · The paper itself

Abstract

The assessment of upper limb (UL) movement patterns plays a critical role in the rehabilitation of individuals with motor impairments resulting from neuromotor disorders, which significantly affect essential activities of daily living (ADLs) such as drinking and eating. However, conventional clinical evaluation methods often lack objective and quantitative insights into the biomechanics of movement. To enable accurate identification of pathological patterns, it is first necessary to establish normative biomechanical and electrophysiological benchmarks in healthy individuals. In this study, a previously developed, low-cost, wearable, and portable prototype device was employed to objectively assess UL movement. The system, specifically designed for clinical applicability, integrates surface electromyography (EMG) sensors and an inertial measurement unit (IMU) to capture muscle activity and kinematic data, respectively. Thirty healthy participants were recruited to perform standardized drinking and eating tasks. The analysis focused on characterizing muscle activation patterns and joint range of motion during different task phases. Results revealed consistent variations in muscle contraction and joint kinematics, allowing the identification of distinct activation profiles for key shoulder muscles. The findings contribute to the establishment of a normative dataset that can serve as a reference for the assessment of clinical populations. Such data are essential for informing rehabilitation strategies and developing predictive models of UL function during ADLs in individuals with neuromotor disorders.

Indexed as

DrinkingEatingElectromyographyUpper ExtremityWearable Electronic DevicesAdultBiomechanical PhenomenaFemaleHumansMaleMovementMuscle ContractionMuscle, SkeletalRange of Motion, ArticularYoung Adultactivities of daily livingbiomechanicselectromyographyinertial measurement unitsupper limbwearable sensors

Identifiers

PMID40942656
PMCPMC12431121

What OpenQuestion holds

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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.