ArticleFrontiers in sports and active living2021
Machine Learning to Improve Orientation Estimation in Sports Situations Challenging for Inertial Sensor Use.
Article in Frontiers in sports and active living, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- How Was Studied the Effect of Manual Wheelchair Configuration on Propulsion Biomechanics: A Systematic Review on Methodologies.Frontiers in rehabilitation sciences · 2022Pooled it
- A Tightly Coupled Multibody Dynamics and Multi-Sensor Fusion Algorithm for Simultaneous Kinematics and Kinetics Estimation.Sensors (Basel, Switzerland) · 2026Article
- Combined Strength of Standardized Lab Sprint Testing and Wheelchair Mobility Field Testing in Wheelchair Tennis Players.American journal of physical medicine & rehabilitation · 2025Article
- Towards an accurate rolling resistance: Estimating intra-cycle load distribution between front and rear wheels during wheelchair propulsion from inertial sensors.Journal of sports sciences · 2024Article
- Shoulder Range of Motion Measurement Using Inertial Measurement Unit-Concurrent Validity and Reliability.Sensors (Basel, Switzerland) · 2023Article
- Using Wearable Sensors to Estimate Mechanical Power Output in Cyclical Sports Other than Cycling-A Review.Sensors (Basel, Switzerland) · 2022Review
- A vision for the future of wearable sensors in spine care and its challenges: narrative review.Journal of spine surgery (Hong Kong) · 2022Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a
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