Evidence map›Paper›PMID 27827880›Full record

ArticleSensors (Basel, Switzerland)2016

Wearable IMU for Shoulder Injury Prevention in Overhead Sports.

Samir A Rawashdeh, Derek A Rafeldt, Timothy L Uhl

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

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

  1. Pooled it
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  15. The Virtual Shoulder Physical Exam.HSS journal : the musculoskeletal journal of Hospital for Special Surgery · 2021
    Article
  16. Article
  17. Accelerometry as an objective measure of upper-extremity activity.Medical & biological engineering & computing · 2021
    Article
  18. The Virtual Shoulder and Knee Physical Examination.Orthopaedic journal of sports medicine · 2020
    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

3 authors.

Samir A RawashdehElectrical and Computer Engineering, University of Michigan, Dearborn, MI 48128, USA. srawa@umich.edu.
Derek A RafeldtAthletic Training and Rehabilitation Sciences, University of Kentucky, Lexington, KY 40536-0200, USA. Derek.rafeldt@uky.edu.
Timothy L UhlAthletic Training and Rehabilitation Sciences, University of Kentucky, Lexington, KY 40536-0200, USA. tluhl2@uky.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Body-worn inertial sensors have enabled motion capture outside of the laboratory setting. In this work, an inertial measurement unit was attached to the upper arm to track and discriminate between shoulder motion gestures in order to help prevent shoulder over-use injuries in athletics through real-time preventative feedback. We present a detection and classification approach that can be used to count the number of times certain motion gestures occur. The application presented involves tracking baseball throws and volleyball serves, which are common overhead movements that can lead to shoulder and elbow overuse injuries. Eleven subjects are recruited to collect training, testing, and randomized validation data, which include throws, serves, and seven other exercises that serve as a large null class of similar movements, which is analogous to a realistic usage scenario and requires a robust estimator.

Indexed as

AthletesBiomechanical PhenomenaElbow InjuriesHumansMonitoring, AmbulatoryRange of Motion, ArticularShoulder Injuriesinertial measurement unitinjury preventionshoulderwearable technology

Identifiers

PMID27827880
PMCPMC5134506

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.