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 post-stroke 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 respective to their body segment. In this article, we propose a new method, which is alignment free and self-calibrated using the 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. Using a two degree of freedom joint mimicking the human elbow, the performance of the algorithm was assessed by comparing the angles obtained from two IMUs to the ones obtained from a marker-based optical tracking system. The self-calibration proved to converge within seconds and the RMS errors with respect to the optical reference system were below 5 degrees. Our method can be particularly useful in the field of telerehabilitation, where precise manual sensor to segment alignment as well as precise, predefined calibration movements are impractical.