ArticleApplied ergonomics2020
Measuring upper arm elevation using an inertial measurement unit: An exploration of sensor fusion algorithms and gyroscope models.
Article in Applied ergonomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transparency as a Means to Analyse the Impact of Inertial Sensors on Users during the Occupational Ergonomic Assessment: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Evaluation of the Angle Deviation between Pilot Holes and Actual Implanted Pedicle Screw Trajectories in Freehand Pedicle Screw Placement for Thoracolumbar Spine Surgery: The Impact of Tapping and Work Experience.Clinics in orthopedic surgery · 2026Article
- Sensor Fusion for Enhancing Motion Capture: Integrating Optical and Inertial Motion Capture Systems.Sensors (Basel, Switzerland) · 2025Article
- Concurrent validity of inertial measurement units in range of motion measurements of upper extremity: A systematic review and meta-analysis.Wearable technologies · 2024Review
- Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist.Sensors (Basel, Switzerland) · 2023Article
- Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities.Sensors (Basel, Switzerland) · 2023Review
- Evaluation of In-Cloth versus On-Skin Sensors for Measuring Trunk and Upper Arm Postures and Movements.Sensors (Basel, Switzerland) · 2023Article
- Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of a New Simplified Inertial Sensor Method against Electrogoniometer for Measuring Wrist Motion in Occupational Studies.Sensors (Basel, Switzerland) · 2022Article
- Manufacturing worker perceptions of using wearable inertial sensors for multiple work shifts.Applied ergonomics · 2022Article
- Diagnosing gait disorders based on angular variations of knee and ankle joints utilizing a developed wearable motion sensor.Healthcare technology letters · 2021Article
- Effects of Sensor Types and Angular Velocity Computational Methods in Field Measurements of Occupational Upper Arm and Trunk Postures and Movements.Sensors (Basel, Switzerland) · 2021Article
- Comparing upper arm and trunk kinematics between manufacturing workers performing predominantly cyclic and non-cyclic work tasks.Applied ergonomics · 2021Article
- A New Quaternion-Based Kalman Filter for Human Body Motion Tracking Using the Second Estimator of the Optimal Quaternion Algorithm and the Joint Angle Constraint Method with Inertial and Magnetic Sensors.Sensors (Basel, Switzerland) · 2020Article
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3 authors.
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Abstract
Many sensor fusion algorithms for analyzing human motion information collected with inertial measurement units have been reported in the scientific literature. Selecting which algorithm to use can be a challenge for ergonomists that may be unfamiliar with the strengths and limitations of the various options. In this paper, we describe fundamental differences among several algorithms, including differences in sensor fusion approach (e.g., complementary filter vs. Kalman Filter) and gyroscope error modeling (i.e., inclusion or exclusion of gyroscope bias). We then compare different sensor fusion algorithms considering the fundamentals discussed using laboratory-based measurements of upper arm elevation collected under three motion speeds. Results indicate peak displacement errors of <4.5° with a computationally efficient, non-proprietary complementary filter that did not account for gyroscope bias during each of the one-minute trials. Controlling for gyroscope bias reduced peak displacement errors to <3.0°. The complementary filters were comparable (<1° peak displacement difference) to the more complex Kalman filters.
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