ArticleJournal of rehabilitation and assistive technologies engineering
Inertial measurement unit-based pose estimation: Analyzing and reducing sensitivity to sensor placement and body measures.
Article in Journal of rehabilitation and assistive technologies engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sensor-to-Segment Calibration Methodologies for Lower-Body Kinematic Analysis with Inertial Sensors: A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- A Multi-Sensor, Multi-Movement Exploratory Study of Motion Tape Strain Data for Low Back Pain Classification.Sensors (Basel, Switzerland) · 2026Article
- Movement-Based Low Back Pain Subgroups Using Motion Tape Strain Data with Biomechanical and Causal Feature Engineering.Sensors (Basel, Switzerland) · 2026Article
- Enhanced Reaction Time Measurement System Based on 3D Accelerometer in Athletics.Sensors (Basel, Switzerland) · 2025Article
- Validation of a Commercially Available IMU-Based System Against an Optoelectronic System for Full-Body Motor Tasks.Sensors (Basel, Switzerland) · 2025Article
- Optimization of MIMU Mounting Position on Shank in Posture Estimation Considering Muscle Protuberance.Sensors (Basel, Switzerland) · 2025Article
- Evaluation of Upper Body and Lower Limbs Kinematics through anSensors (Basel, Switzerland) · 2023Article
- Article
- Estimation of 3D Body Center of Mass Acceleration and Instantaneous Velocity from a Wearable Inertial Sensor Network in Transfemoral Amputee Gait: A Case Study.Sensors (Basel, Switzerland) · 2021Article
- Quantifying varus thrust in knee osteoarthritis using wearable inertial sensors: A proof of concept.Clinical biomechanics (Bristol, Avon) · 2020Article
- Optimization of IMU Sensor Placement for the Measurement of Lower Limb Joint Kinematics.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionInertial measurement units have been proposed for automated pose estimation and exercise monitoring in clinical settings. However, many existing methods assume an extensive calibration procedure, which may not be realizable in clinical practice. In this study, an inertial measurement unit-based pose estimation method using extended Kalman filter and kinematic chain modeling is adapted for lower body pose estimation during clinical mobility tests such as the single leg squat, and the sensitivity to parameter calibration is investigated.
methodsThe sensitivity of pose estimation accuracy to each of the kinematic model and sensor placement parameters was analyzed. Sensitivity analysis results suggested that accurate extraction of inertial measurement unit orientation on the body is a key factor in improving the accuracy. Hence, a simple calibration protocol was proposed to reach a better approximation for inertial measurement unit orientation.
resultsAfter applying the protocol, the ankle, knee, and hip joint angle errors improved to
conclusionsOnly a small subset of kinematic and sensor parameters contribute significantly to pose estimation accuracy when using body worn inertial sensors. A simple calibration procedure identifying the inertial measurement unit orientation on the body can provide good pose estimation performance.
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