Alignment-Free, Self-Calibrating Elbow Angles Measurement Using Inertial Sensors

Due to their relative ease of handling and low cost, inertial measurement unit (IMU)-based joint angle measurements are used for a widespread range of applications. These include sports performance, gait analysis, and rehabilitation (e.g., Parkinson's disease monitoring or poststroke assessment...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2017-03, Vol.21 (2), p.312-319
Main Authors: Muller, Philipp, Begin, Marc-Andre, Schauer, Thomas, Seel, Thomas
Format: Article
Language:English
Subjects:
Adult
Algorithms
Alignment
Angular velocity
Arm
Axes of rotation
Biomechanical Phenomena - physiology
Biomedical measurement
Biomedical optical imaging
Calibration
Computational Biology - methods
Cost function
Elbow
Elbow (anatomy)
Elbow - physiology
Elbow tracking
Gait
Human motion
Humans
inertial measurement units (IMU)
Inertial platforms
Inertial sensing devices
Kinematics
Male
Motion capture
Movement disorders
Neurodegenerative diseases
Optical sensors
Optical tracking
Optimization
Parkinson's disease
Physiology - methods
Range of Motion, Articular - physiology
Reference systems
Rehabilitation
Self calibration
Tracking systems
two degree of freedom (2DOF) joint
upper limb motion
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Description
Summary:Due to their relative ease of handling and low cost, inertial measurement unit (IMU)-based joint angle measurements are used for a widespread range of applications. These include sports performance, gait analysis, and rehabilitation (e.g., Parkinson's disease monitoring or poststroke assessment). However, a major downside of current algorithms, recomposing human kinematics from IMU data, is that they require calibration motions and/or the careful alignment of the IMUs with respect to the body segments. In this article, we propose a new method, which is alignment-free and self-calibrating using arbitrary movements of the user and an initial zero reference arm pose. The proposed method utilizes real-time optimization to identify the two dominant axes of rotation of the elbow joint. The performance of the algorithm was assessed in an optical motion capture laboratory. The estimated IMU-based angles of a human subject were compared to the ones from a marker-based optical tracking system. The self-calibration converged in under 9.5 s on average and the rms errors with respect to the optical reference system were 2.7° for the flexion/extension and 3.8° for the pronation/supination angle. Our method can be particularly useful in the field of rehabilitation, where precise manual sensor-to-segment alignment as well as precise, predefined calibration movements are impractical.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2016.2639537