Evidence map›Paper›PMID 41064393›Full record

ArticleWearable technologies2025

Using a wearable system combining inertial and force sensing for simultaneous detection of limb motion and grasping actions in the workplace.

Matteo Musso, Shaoping Bai, Anderson Oliveira

Abstract read
In one paragraph

Article in Wearable technologies, 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

3 authors.

Matteo MussoDepartment of Materials and Production, Aalborg University, Aalborg, Denmark.ORCID https://orcid.org/0000-0002-1535-9521
Shaoping BaiDepartment of Materials and Production, Aalborg University, Aalborg, Denmark.
Anderson OliveiraDepartment of Materials and Production, Aalborg University, Aalborg, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using wearable sensors to evaluate workers' performance is challenging with existing sensor techniques. It requires detecting not only limb motions but also the onset and offset of specific actions. Commonly used inertial measurement units (IMUs) can be combined with surface electromyography (sEMG) to detect muscular activity. However, sEMG requires skin preparation and careful sensor placement, and can be affected by sweat or motion artifacts. To address these limitations, we used a wearable system combining IMUs and force-sensing resistors (FSRs), where IMUs capture joint kinematics and FSRs detect grasping actions. The system included three IMUs (on the trunk, upper arm, and forearm) and two FSR arrays (on the upper and lower arms). The system was first validated in a laboratory setting against an optical motion capture system with 10 healthy young adults performing isolated upper limb movements and mimicking lifting tasks. The results showed high agreement in joint angle estimation (coefficient of multiple correlation = 0.95 0.04), with a maximum root mean square error of 8.7 2.92°, and a mean absolute timing error for grasp detection of -0.59 seconds. To evaluate its applicability in real-world scenarios, a pilot in-field test was then conducted with two manufacturing workers (using and not using a passive shoulder exoskeleton) during a repetitive panel-packing task. The test shows highly consistent grasping detection, which allowed segmenting the task with a small variability in task duration (maximum coefficient of variation = 5.16). These findings demonstrate the feasibility of using the proposed method in industrial environments to analyze upper limb motion and grasping activity.

Indexed as

exoskeletonFSRIMUmotion trackingwearable sensors

Identifiers

PMID41064393
PMCPMC12501534

What OpenQuestion holds

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LicenceCC BY
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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.