ArticleFrontiers in bioengineering and biotechnology2025
Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions.
Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Research progress on digital-twin-driven full life-cycle design and manufacturing of rehabilitation assistive devices.Frontiers in bioengineering and biotechnology · 2026Review
- Multimodal inverse kinematics significantly improves IMU-based biomechanical analyses.Scientific reports · 2025Article
- Computer Model Based on an Asynchronous BLE 5.0 IMU Sensor Network for Biomechanical Applications.Sensors (Basel, Switzerland) · 2025Article
- DIODEM - A Diverse Inertial and Optical Dataset of kinEmatic chain Motion.Scientific data · 2025Article
- Evaluating Sparse Inertial Measurement Unit Configurations for Inferring Treadmill Running Motion.Sensors (Basel, Switzerland) · 2025Article
- Article
- Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations.Frontiers in bioengineering and biotechnology · 2024Article
- Challenges and advances in the use of wearable sensors for lower extremity biomechanics.Journal of biomechanics · 2023Article
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Authors and funding
8 authors.
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Abstract
Estimating spatiotemporal, kinematic, and kinetic movement variables with little obtrusion to the user is critical for clinical and sports applications. One possible approach is using a sparse inertial sensor setup, where sensors are not placed on all relevant body segments. Here, we investigated if movement variables can be estimated similarly accurate from sparse sensor setups as from a full lower-body sensor setup. We estimated the variables by solving optimal control problems with sagittal plane lower-body musculoskeletal models, in which we minimized an objective that combined tracking of accelerometer and gyroscope data with minimizing muscular effort. We created simulations for 10 participants at three walking and three running speeds, using seven sensor setups with between two and seven sensors located at the feet, shank, thighs, and/or pelvis. We found that differences between variables estimated from inertial sensors and those from optical motion capture were small for all sensor setups. Including all sensors did not necessarily lead to the smallest root mean square deviations (RMSDs) and highest coefficients of determination (
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