ArticleJournal of biomechanics2016
Gait biomechanics in the era of data science.
Article in Journal of biomechanics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it, 102 citations in OpenAlex.
- Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review.Frontiers in neurorobotics · 2022Pooled it
- Meta-Analysis of Integrated Therapeutic Methods in Noninvasive Lower Back Pain Therapy (LBP): The Role of Interdisciplinary Functional Diagnostics.Pain research & management · 2020Pooled it
- Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach.Sensors (Basel, Switzerland) · 2018Trial
- A Computational Pipeline for Activity Prediction Using Wearable Sensor Data.Research square · 2026Article
- Artificial Intelligence and Machine Learning in Diabetic Foot Ulcer Care: Advances in Diagnosis, Treatment, Prognosis, and Novel Therapeutic Strategies.Journal of diabetes science and technology · 2025Review
- Integrating personalized shape prediction, biomechanical modeling, and wearables for bone stress prediction in runners.NPJ digital medicine · 2025Article
- A Biomechanical Dataset of 1,798 Healthy and Injured Subjects During Treadmill Walking and Running.Scientific data · 2024Article
- Machine learning in knee injury sequelae detection: Unravelling the role of psychological factors and preventing long-term sequelae.Journal of experimental orthopaedics · 2024Article
- Explainable AI Elucidates Musculoskeletal Biomechanics: A Case Study Using Wrist Surgeries.Annals of biomedical engineering · 2024Article
- Modern three-dimensional digital methods for studying locomotor biomechanics in tetrapods.The Journal of experimental biology · 2023Review
- Use of subject-specific models to detect fatigue-related changes in running biomechanics: a random forest approach.Frontiers in sports and active living · 2023Article
- Running in the wild: Energetics explain ecological running speeds.Current biology : CB · 2022Article
- Predicting knee adduction moment response to gait retraining with minimal clinical data.PLoS computational biology · 2022Article
- Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease.Frontiers in neurology · 2022Review
- Identification of Patients with Similar Gait Compensating Strategies Due to Unilateral Hip Osteoarthritis and the Effect of Total Hip Replacement: A Secondary Analysis.Journal of clinical medicine · 2021Article
- Machine-Learning Based Determination of Gait Events from Foot-Mounted Inertial Units.Sensors (Basel, Switzerland) · 2021Article
- Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis.Frontiers in neurology · 2021Article
- An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy.Journal of clinical medicine · 2020Article
- Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Data science has transformed fields such as computer vision and economics. The ability of modern data science methods to extract insights from large, complex, heterogeneous, and noisy datasets is beginning to provide a powerful complement to the traditional approaches of experimental motion capture and biomechanical modeling. The purpose of this article is to provide a perspective on how data science methods can be incorporated into our field to advance our understanding of gait biomechanics and improve treatment planning procedures. We provide examples of how data science approaches have been applied to biomechanical data. We then discuss the challenges that remain for effectively using data science approaches in clinical gait analysis and gait biomechanics research, including the need for new tools, better infrastructure and incentives for sharing data, and education across the disciplines of biomechanics and data science. By addressing these challenges, we can revolutionize treatment planning and biomechanics research by capitalizing on the wealth of knowledge gained by gait researchers over the past decades and the vast, but often siloed, data that are collected in clinical and research laboratories around the world.
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What OpenQuestion holds
Registered trials
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.