Evidence map›Paper›PMID 37423120›Full record

ArticleJournal of biomechanics2023

Challenges and advances in the use of wearable sensors for lower extremity biomechanics.

Jocelyn F Hafer, Rachel Vitali, Reed Gurchiek, Carolin Curtze, Peter Shull, Stephen M Cain

Open access · greenAbstract read
In one paragraph

Article in Journal of biomechanics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
4.4field-weighted citation impact, top 5% 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

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.

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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

6 authors at 6 institutions in 2 countries.

Jocelyn F HaferDepartment of Kinesiology and Applied Physiology, University of Delaware, Newark, DE, United States. Electronic address: jfhafer@udel.edu.
Rachel VitaliDepartment of Mechanical Engineering, University of Iowa, Iowa City, IA, United States.
Reed GurchiekDepartment of Bioengineering, Stanford University, Stanford, CA, United States.
Carolin CurtzeDepartment of Biomechanics, University of Nebraska at Omaha, Omaha, NE, United States.
Peter ShullState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, China.
Stephen M CainDepartment of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, United States.
Shanghai Jiao Tong University · CNStanford University · USUniversity of Delaware · USUniversity of Iowa · USUniversity of Nebraska at Omaha · USWest Virginia University · US

Funding

Visual control of locomotion in people with Parkinsons diseaseP20GM109090 · NIGMS · UNIVERSITY OF NEBRASKA OMAHA · PI STERGIOU, NIKOLAOS · 2014 to 2023
$20.5M
Understanding how age, knee osteoarthritis, and symptoms influence the structure and variance of real-world gait mechanicsR21AG076989 · NIA · UNIVERSITY OF DELAWARE · PI HAFER, JOCELYN FREY · 2022 to 2023
$435k
NIA NIH HHS R21 AG076989NIGMS NIH HHS P20 GM109090
6 · The paper itself

Abstract

The use of wearable sensors for the collection of lower extremity biomechanical data is increasing in popularity, in part due to the ease of collecting data and the ability to capture movement outside of traditional biomechanics laboratories. Consequently, an increasing number of researchers are facing the challenges that come with utilizing the data captured by wearable sensors. These challenges include identifying/calculating meaningful measures from unfamiliar data types (measures of acceleration and angular velocity instead of positions and joint angles), defining sensor-to-segment alignments for calculating traditional biomechanics metrics, using reduced sensor sets and machine learning to predict unmeasured signals, making decisions about when and how to make algorithms freely available, and developing or replicating methods to perform basic processing tasks such as recognizing activities of interest or identifying gait events. In this perspective article, we present our own approaches to common challenges in lower extremity biomechanics research using wearable sensors and share our perspectives on approaching several of these challenges. We present these perspectives with examples that come mostly from gait research, but many of the concepts also apply to other contexts where researchers may use wearable sensors. Our goal is to introduce common challenges to new users of wearable sensors, and to promote dialogue amongst experienced users towards best practices.

Indexed as

MovementWearable Electronic DevicesAccelerationBiomechanical PhenomenaGaitLower ExtremityGaitInertial measurement unitsOpen-sourceReal-worldSensor-to-segment

Identifiers

PMID37423120
PMCPMC10529245
OpenAlexW4383104138

What OpenQuestion holds

Textmetadata
LicenceTDM
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.