scholarly journals Angular Motion Estimation Using Dynamic Models in a Gyro-Free Inertial Measurement Unit

Sensors ◽  
2012 ◽  
Vol 12 (5) ◽  
pp. 5310-5327 ◽  
Author(s):  
Ezzaldeen Edwan ◽  
Stefan Knedlik ◽  
Otmar Loffeld
Keyword(s):  
Motion Estimation ◽  
Inertial Measurement Unit ◽  
Dynamic Models ◽  
Angular Motion ◽  
Measurement Unit ◽  
Inertial Measurement

Decoupled Scalar Approach for Aircraft Angular Motion Estimation Using a Gyro-Free Inertial Measurement Unit

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Ali Dehghan Manshadi ◽  
Fariborz Saghafi
Keyword(s):  
Motion Estimation ◽  
Inertial Measurement Unit ◽  
Measurement Data ◽  
Estimation Algorithm ◽  
Monte Carlo Analysis ◽  
Angular Motion ◽  
Measurement Unit ◽  
Scalar Form ◽  
Inertial Measurement ◽  
Aircraft Dynamics

Abstract In-flight aircraft angular motion estimation based on an all-accelerometers inertial measurement unit (IMU) is investigated in this study. The relative acceleration equation as the representative of a rigid-body kinematics is manipulated to present the state and measurement equations of the aircraft dynamics. A decoupled scalar form (DSF) of the system equations, as a free-singularity problem, is derived. Mathematical modeling and simulation of an aircraft dynamics, equipped with an all-accelerometers IMU, are employed to prepare measurement data. Taking into account the modeling of accelerometer error, the measurement data have been simulated as a real condition. Three extended Kalman filters (EKFs) are used in parallel to estimate aircraft angular motion. Performance of the estimation algorithm is assessed by Monte Carlo analysis. As a result, the presented decoupled scalar approach using a gyro-free IMU (GF-IMU) provides an uncorrelated estimate of the in-flight aircraft motion components.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

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