Evidence map›Paper›PMID 32707987›Full record

ArticleSensors (Basel, Switzerland)2020

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications.

Lin Zhou, Eric Fischer, Can Tunca, Clemens Markus Brahms, Cem Ersoy, Urs Granacher, Bert Arnrich

Registry-linked trialAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06509542 (Measuring the Concurrent Validity of the Upper Limb Use Ratio With Accelerometers in an Ecological Situation After Stroke. ValidFUR), which is not on this map. Cited by 48 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 2 pooled it
–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.

NCT06509542 nacompletednot on this mapstarted 2024, after this paper: background citation

Measuring the Concurrent Validity of the Upper Limb Use Ratio With Accelerometers in an Ecological Situation After Stroke. ValidFUR

TypeinterventionalSponsorUniversity Hospital, MontpellierRan2024 to 2025Enrolled25ConditionsCerebrovascular AccidentArmsObjective assessment of upper limb use during activities in ecological situation
3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Observational
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. 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

7 authors.

Lin ZhouDigital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.ORCID 0000-0001-9916-3878
Eric FischerDigital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.
Can TuncaNETLAB, Department of Computer Engineering, Bogazici University, 34342 Istanbul, Turkey.ORCID 0000-0002-7532-8428
Clemens Markus BrahmsDivision of Training and Movement Sciences, University of Potsdam, 14469 Potsdam, Germany.
Cem ErsoyNETLAB, Department of Computer Engineering, Bogazici University, 34342 Istanbul, Turkey.
Urs GranacherDivision of Training and Movement Sciences, University of Potsdam, 14469 Potsdam, Germany.ORCID 0000-0002-7095-813X
Bert ArnrichDigital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany.

Funding

Bundesministerium für Bildung und Forschung 01IS19066
6 · The paper itself

Abstract

Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.

Indexed as

AccelerationGaitAdultAlgorithmsDelivery of Health CareFemaleHumansMaleMovementYoung Adultcomparison of devicesgait analysisinertial measurement unitpervasive healthcare

Identifiers

PMID32707987
PMCPMC7435687

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.