ArticleSensors (Basel, Switzerland)2020
Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis 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.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review.Frontiers in neurorobotics · 2022Pooled it
- Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking.Biomedical engineering online · 2025Article
- Integrating personalized shape prediction, biomechanical modeling, and wearables for bone stress prediction in runners.NPJ digital medicine · 2025Article
- The running gait analysis technology: A comprehensive systematic literature review.Journal of orthopaedics · 2025Review
- Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder.Heliyon · 2025Article
- Article
- Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions.Frontiers in bioengineering and biotechnology · 2025Article
- E-Textiles for Sports and Fitness Sensing: Current State, Challenges, and Future Opportunities.Sensors (Basel, Switzerland) · 2024Review
- Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running.Sensors (Basel, Switzerland) · 2024Article
- Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors.Frontiers in bioengineering and biotechnology · 2024Review
- 3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units.Frontiers in bioengineering and biotechnology · 2024Article
- Article
- Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model.Sensors (Basel, Switzerland) · 2023Article
- BioMAT: An Open-Source Biomechanics Multi-Activity Transformer for Joint Kinematic Predictions Using Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors.Scientific reports · 2023Article
- Estimation of horizontal running power using foot-worn inertial measurement units.Frontiers in bioengineering and biotechnology · 2023Article
- Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.Frontiers in bioengineering and biotechnology · 2023Article
- Development of Tiny Vane-Type Magnetorheological Brake Considering Quality Function Deployment.Micromachines · 2022Article
- Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait.Frontiers in neuroscience · 2022Review
- Examination of a foot mounted IMU-based methodology for a running gait assessment.Frontiers in sports and active living · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Abnormal running kinematics are associated with an increased incidence of lower extremity injuries among runners. Accurate and unobtrusive running kinematic measurement plays an important role in the detection of gait abnormalities and the prevention of injuries among runners. Inertial-based methods have been proposed to address this need. However, previous methods require cumbersome sensor setup or participant-specific calibration. This study aims to validate a shoe-mounted accelerometer for sagittal plane lower extremity angle measurement during running based on a deep learning approach. A convolutional neural network (CNN) architecture was selected as the regression model to generalize in inter-participant scenarios and to minimize poorly estimated joints. Motion and accelerometer data were recorded from ten participants while running on a treadmill at five different speeds. The reference joint angles were measured by an optical motion capture system. The CNN model predictions deviated from the reference angles with a root mean squared error (RMSE) of less than 3.5° and 6.5° in intra- and inter-participant scenarios, respectively. Moreover, we provide an estimation of six important gait events with a mean absolute error of less than 2.5° and 6.5° in intra- and inter-participants scenarios, respectively. This study highlights an appealing minimal sensor setup approach for gait analysis purposes.
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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.