Dataset Information

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

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.

SUBMITTER: Halilaj E

PROVIDER: S-EPMC6879187 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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

Halilaj Eni E Rajagopal Apoorva A Fiterau Madalina M Hicks Jennifer L JL Hastie Trevor J TJ Delp Scott L SL

Journal of biomechanics 20180913

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 literatu ...[more]

PMID: 30279002

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Dataset Information

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

Publications

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

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