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

• Published in: IEEE journal of biomedical and health informatics Vol. 21; no. 2; pp. 312 - 319
• Main Authors: Muller, Philipp, Begin, Marc-Andre, Schauer, Thomas, Seel, Thomas
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2016.2639537

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<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> for the flexion/extension and 3.8<inline-formula><tex-math notation="LaTeX">^\circ</tex-math></inline-formula> 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.