ArticleAnnals of biomedical engineering2021
Estimating Knee Joint Load Using Acoustic Emissions During Ambulation.
Article in Annals of biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on Evaluation Strategies for Field Assessment of Upper-Body Industrial Exoskeletons: Current Practices and Future Trends.Annals of biomedical engineering · 2022Pooled it
- Wireless knee joint monitoring using biodegradable single-ended pressure sensor in osteoarthritis management.Science advances · 2026Article
- Wearable sensing for understanding and influencing human movement in ecological contexts.Current opinion in biomedical engineering · 2023Article
- Classifying Pre-Radiographic Osteoarthritis of the Knee Using Wearable Acoustics Sensing at the Point of Care.IEEE sensors journal · 2023Article
- Ten steps to becoming a musculoskeletal simulation expert: A half-century of progress and outlook for the future.Journal of biomechanics · 2023Review
- Limiting the Use of Electromyography and Ground Reaction Force Data Changes the Magnitude and Ranking of Modelled Anterior Cruciate Ligament Forces.Bioengineering (Basel, Switzerland) · 2023Article
- Effect of Assistance Using a Bilateral Robotic Knee Exoskeleton on Tibiofemoral Force Using a Neuromuscular Model.Annals of biomedical engineering · 2022Article
- A Feasibility Study on Tribological Origins of Knee Acoustic Emissions.IEEE transactions on bio-medical engineering · 2022Article
- Engineering Aspects of Incidence, Prevalence, and Management of Osteoarthritis: A Review.Annals of biomedical engineering · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Quantifying joint load in activities of daily life could lead to improvements in mobility for numerous people; however, current methods for assessing joint load are unsuitable for ubiquitous settings. The aim of this study is to demonstrate that joint acoustic emissions contain information to estimate this internal joint load in a potentially wearable implementation. Eleven healthy, able-bodied individuals performed ambulation tasks under varying speed, incline, and loading conditions while joint acoustic emissions and essential gait measures-electromyography, ground reaction forces, and motion capture trajectories-were collected. The gait measures were synthesized using a neuromuscular model to estimate internal joint contact force which was the target variable for subject-specific machine learning models (XGBoost) trained based on spectral, temporal, cepstral, and amplitude-based features of the joint acoustic emissions. The model using joint acoustic emissions significantly outperformed (p < 0.05) the best estimate without the sounds, the subject-specific average load (MAE = 0.31 ± 0.12 BW), for both seen (MAE = 0.08 ± 0.01 BW) and unseen (MAE = 0.21 ± 0.05 BW) conditions. This demonstrates that joint acoustic emissions contain information that correlates to internal joint contact force and that information is consistent such that unique cases can be estimated.
Indexed as
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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.