ArticleSensors (Basel, Switzerland)2018
Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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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
32 citing papers in PubMed, 3 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Validity and Reliability of Inertial Measurement Units on Lower Extremity Kinematics During Running: A Systematic Review and Meta-Analysis.Sports medicine - open · 2022Pooled it
- Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review.Frontiers in neurorobotics · 2022Pooled it
- Does Site Matter? Impact of Inertial Measurement Unit Placement on the Validity and Reliability of Stride Variables During Running: A Systematic Review and Meta-analysis.Sports medicine (Auckland, N.Z.) · 2021Pooled it
- Validation of Novel Stride Length Model-Based Approaches to Estimate Distance Covered Based on Acceleration and Pressure Data During Walking.Sensors (Basel, Switzerland) · 2025Article
- Limited Interchangeability of Smartwatches and Lace-Mounted IMUs for Running Gait Analysis.Sensors (Basel, Switzerland) · 2025Article
- Influence of Carbon-fiber Shoes on Outdoor Running Biomechanics as Assessed with Wearable Sensors.International journal of exercise science · 2025Article
- The Measurement of Spatiotemporal Parameters in Running at Different Velocities: A Comparison Between a GPS Unit and an Infrared Mat.Methods and protocols · 2024Article
- Enhancing Intelligent Shoes with Gait Analysis: A Review on the Spatiotemporal Estimation Techniques.Sensors (Basel, Switzerland) · 2024Review
- Maximum velocity and leg-specific ground reaction force production change with radius during flat curve sprinting.The Journal of experimental biology · 2024Article
- Article
- Gait Stride Length Estimation Using Embedded Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Foot Pronation Prediction with Inertial Sensors during Running: A Preliminary Application of Data-Driven Approaches.Journal of human kinetics · 2023Article
- Internet-of-Things-Enabled Markerless Running Gait Assessment from a Single Smartphone Camera.Sensors (Basel, Switzerland) · 2023Article
- Wearable Leg Movement Monitoring System for High-Precision Real-Time Metabolic Energy Estimation and Motion Recognition.Research (Washington, D.C.) · 2023Article
- Estimation of Foot Trajectory and Stride Length during Level Ground Running Using Foot-Mounted Inertial Measurement Units.Sensors (Basel, Switzerland) · 2022Article
- Estimating Running Ground Reaction Forces from Plantar Pressure during Graded Running.Sensors (Basel, Switzerland) · 2022Article
- Inertial Sensor-to-Segment Calibration for Accurate 3D Joint Angle Calculation for Use in OpenSim.Sensors (Basel, Switzerland) · 2022Article
- Drift-Free 3D Orientation and Displacement Estimation for Quasi-Cyclical Movements Using One Inertial Measurement Unit: Application to Running.Sensors (Basel, Switzerland) · 2022Article
- Stride Lengths during Maximal Linear Sprint Acceleration Obtained with Foot-Mounted Inertial Measurement Units.Sensors (Basel, Switzerland) · 2022Article
- Evaluating footwear "in the wild": Examining wrap and lace trail shoe closures during trail running.Frontiers in sports and active living · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy of four algorithms, which calculate the stride velocity and stride length during running using data of an inertial measurement unit (IMU) placed in the midsole of a running shoe. The four algorithms are based on stride time, foot acceleration, foot trajectory estimation, and deep learning, respectively. They are compared using two studies: a laboratory-based study comprising 2377 strides from 27 subjects with 3D motion tracking as a reference and a field study comprising 12 subjects performing a 3.2-km run in a real-world setup. The results show that the foot trajectory estimation algorithm performs best, achieving a mean error of 0.032 ± 0.274 m/s for the velocity estimation and 0.022 ± 0.157 m for the stride length. An interesting alternative for systems with a low energy budget is the acceleration-based approach. Our results support the implementation decision for running velocity and distance tracking using IMUs embedded in the sole of a running shoe.
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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.