Evidence map›Paper›PMID 35458993›Full record

SynthesisSensors (Basel, Switzerland)2022

Accelerometer-Based Identification of Fatigue in the Lower Limbs during Cyclical Physical Exercise: A Systematic Review.

Luca Marotta, Bouke L Scheltinga, Robbert van Middelaar, Wichor M Bramer, Bert-Jan F van Beijnum, Jasper Reenalda, Jaap H Buurke

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.5field-weighted citation impact, top 7% of its field
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

10 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. The effect of exercise-induced muscle fatigue on gait parameters among older adults: a systematic review and meta-analysis.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2025
    Review
  3. Article
  4. Review
  5. Review
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  7. Article
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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

7 authors at 3 institutions in 1 country.

Luca MarottaRoessingh Research and Development, 7522 AH Enschede, The Netherlands.ORCID 0000-0002-2269-298X
Bouke L ScheltingaRoessingh Research and Development, 7522 AH Enschede, The Netherlands.
Robbert van MiddelaarDepartment of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands.
Wichor M BramerMedical Library, Erasmus University Medical Center, 3000 CA Rotterdam, The Netherlands.
Bert-Jan F van BeijnumDepartment of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands.
Jasper ReenaldaRoessingh Research and Development, 7522 AH Enschede, The Netherlands.
Jaap H BuurkeRoessingh Research and Development, 7522 AH Enschede, The Netherlands.
Roessingh Research and Development · NLUniversity of Twente · NLErasmus MC · NL

Funding

Horizon 2020 Framework Programme of the European Union for Research and Innovation 826304
6 · The paper itself

Abstract

Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson's correlation with an RPE (rate of perceived exertion) between

Indexed as

RunningAccelerometryBiomechanical PhenomenaExerciseFatigueHumansLower Extremityartificial intelligencebiomechanical phenomenahuman movementinertial measurement unitsphysical activityrunningwalking

Identifiers

PMID35458993
PMCPMC9025833
OpenAlexW4224234941

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.