Evidence map›Paper›PMID 30279002›Full record

ReviewJournal of biomechanics2018

Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities.

Eni Halilaj, Apoorva Rajagopal, Madalina Fiterau, Jennifer L Hicks, Trevor J Hastie, Scott L Delp

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of biomechanics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 177 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
177citing papers in PubMed, 6 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

177 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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117 more citing papers are in PubMed but not listed here.

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.

Eni HalilajDepartment of Mechanical Engineering, Carnegie Mellon University, United States. Electronic address: ehalilaj@andrew.cmu.edu.
Apoorva RajagopalDepartment of Mechanical Engineering, Stanford University, United States.
Madalina FiterauDepartment of Computer Science, Stanford University, United States.
Jennifer L HicksDepartment of Bioengineering, Stanford University, United States.
Trevor J HastieDepartment of Statistics, Stanford University, United States; Department of Health Research and Policy, Stanford University, United States.
Scott L DelpDepartment of Mechanical Engineering, Stanford University, United States; Department of Bioengineering, Stanford University, United States; Department of Orthopaedic Surgery, Stanford University, United States.

Funding

Training CoreU54EB020405 · NIBIB · STANFORD UNIVERSITY · PI DELP, SCOTT L · 2014 to 2018
$11.6M
NIBIB NIH HHS U54 EB020405
6 · The paper itself

Abstract

Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.

Indexed as

Machine LearningBiomechanical PhenomenaHumansMovementData scienceMachine learningMusculoskeletalNeuromuscular

Identifiers

PMID30279002
PMCPMC6879187

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.