Evidence map›Paper›PMID 37698766›Full record

SynthesisSports medicine (Auckland, N.Z.)2023

The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review.

T Alexander Swain, Melitta A McNarry, Adam W H Runacres, Kelly A Mackintosh

Abstract readSystematic Review
In one paragraph

Synthesis in Sports medicine (Auckland, N.Z.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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.

T Alexander SwainApplied Sports, Technology, Exercise and Medicine Research Centre (A-STEM), Swansea University, Swansea, UK.ORCID 0000-0003-3142-2399
Melitta A McNarryApplied Sports, Technology, Exercise and Medicine Research Centre (A-STEM), Swansea University, Swansea, UK.ORCID 0000-0003-0813-7477
Adam W H RunacresInstitute of Sport, Manchester Metropolitan University, Manchester, UK.ORCID 0000-0002-8251-2805
Kelly A MackintoshApplied Sports, Technology, Exercise and Medicine Research Centre (A-STEM), Swansea University, Swansea, UK. k.mackintosh@swansea.ac.uk.ORCID 0000-0003-0355-6357

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMovement quality is typically assessed by drawing comparisons against predetermined movement standards. Movements are often discretely scored or labelled against pre-set criteria, though movement quality can also be evaluated using motion-related measurements (e.g., spatio-temporal parameters and kinematic variables). Wearable technology has the potential to measure and assess movement quality and offer valuable, practical feedback.

objectivesA systematic approach was taken to examine the benefits associated with multi-sensor and multiple wearable-device usage, compared with unimodal applications, when assessing movement quality. Consequently, this review considers the additional variables and features that could be obtained through multi-sensor devices for use in movement analyses. Processing methods and applications of the various configurations were also explored.

methodsArticles were included within this review if they were written in English, specifically studied the use of wearable sensors to assess movement quality, and were published between January 2010 and December 2022. Of the 62,635 articles initially identified, 27 papers were included in this review. The quality of included studies was determined using a modified Downs and Black checklist, with 24/27 high quality.

resultsFifteen of the 27 included studies used a classification approach, 11 used a measurement approach, and one used both methods. Accelerometers featured in all 27 studies, in isolation (n = 5), with a gyroscope (n = 9), or with both a gyroscope and a magnetometer (n = 13). Sampling frequencies across all studies ranged from 50 to 200 Hz. The most common classification methods were traditional feature-based classifiers (n = 5) and support vector machines (SVM; n = 5). Sensor fusion featured in six of the 16 classification studies and nine of the 12 measurement studies, with the Madgwick algorithm most prevalent (n = 7).

conclusionsThis systematic review highlights the differences between the applications and processing methods associated with the use of unimodal and multi-sensor wearable devices when assessing movement quality. Further, the use of multiple devices appears to increase the feasibility of effectively assessing holistic movements, while multi-sensor devices offer the ability to obtain more output metrics.

Indexed as

MovementWearable Electronic DevicesBiomechanical PhenomenaHumans

Identifiers

PMID37698766
PMCPMC10687099

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.