Evidence map›Paper›PMID 25954954›Full record

ArticleSensors (Basel, Switzerland)2015

Accuracy of a custom physical activity and knee angle measurement sensor system for patients with neuromuscular disorders and gait abnormalities.

Frank Feldhege, Anett Mau-Moeller, Tobias Lindner, Albert Hein, Andreas Markschies, Uwe Klaus Zettl, Rainer Bader

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing 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.

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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Observational
  11. Article
  12. Article
  13. Article
  14. 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.

Frank FeldhegeDepartment of Orthopaedics, University Medicine Rostock, Doberaner Str. 142, 18057 Rostock, Germany. frank.feldhege@med.uni-rostock.de.
Anett Mau-MoellerDepartment of Orthopaedics, University Medicine Rostock, Doberaner Str. 142, 18057 Rostock, Germany. anett.mau-moeller@med.uni-rostock.de.
Tobias LindnerDepartment of Orthopaedics, University Medicine Rostock, Doberaner Str. 142, 18057 Rostock, Germany. tobias.lindner@med.uni-rostock.de.
Albert HeinInstitute of Computer Science, University of Rostock, Albert-Einstein-Str. 22, Rostock 18059, Germany. albert.hein@uni-rostock.de.
Andreas MarkschiesMedizintechnik Rostock GmbH, Zur Himmelspforte 1, Rostock 18055, Germany. a.markschies@mtronline.de.
Uwe Klaus ZettlDepartment of Neurology, University Medicine Rostock, Gehlsheimer Str. 20, Rostock 18147, Germany. uwe.zettl@med.uni-rostock.de.
Rainer BaderDepartment of Orthopaedics, University Medicine Rostock, Doberaner Str. 142, 18057 Rostock, Germany. rainer.bader@med.uni-rostock.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long-term assessment of ambulatory behavior and joint motion are valuable tools for the evaluation of therapy effectiveness in patients with neuromuscular disorders and gait abnormalities. Even though there are several tools available to quantify ambulatory behavior in a home environment, reliable measurement of joint motion is still limited to laboratory tests. The aim of this study was to develop and evaluate a novel inertial sensor system for ambulatory behavior and joint motion measurement in the everyday environment. An algorithm for behavior classification, step detection, and knee angle calculation was developed. The validation protocol consisted of simulated daily activities in a laboratory environment. The tests were performed with ten healthy subjects and eleven patients with multiple sclerosis. Activity classification showed comparable performance to commercially available activPAL sensors. Step detection with our sensor system was more accurate. The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer. This new system combines ambulatory behavior assessment and knee angle measurement for long-term measurement periods in a home environment. The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting.

Indexed as

AccelerometryActivities of Daily LivingAdultAlgorithmsEquipment DesignFemaleGait Disorders, NeurologicHumansKnee JointMaleMonitoring, AmbulatoryMultiple SclerosisRange of Motion, ArticularSignal Processing, Computer-AssistedYoung Adult

Identifiers

PMID25954954
PMCPMC4482003

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.