05/2018 – 10/2019
Inertial measurement units (IMUs) have proven to be useful devices for orientation measurement, which plays a critical role in fields like aerospace, robotics and navigation. In the context of healthcare, IMUs provide a low cost alternative for human activity recognition and motion analysis . For example, the calculation of knee angles based on inertial sensors can be used for rehabilitation after sport injuries . For the precise computation of kinematic parameters like angles and trajectories, algorithms are required which need to be validated, especially for clinical measurements. Typically, algorithms for sensor orientation estimation employ strap-down integration of the angular rates to obtain a first estimate of the orientation . However, the estimation error grows over time due to quantization, integration and sensor errors . Therefore, a careful choice of algorithms is indispensable to improve the estimation accuracy .
The purpose of this Bachelor’s thesis is the evaluation of IMU orientation and angle computation algorithms using a gimbal apparatus that needs to be developed. The proposed apparatus and methodology will be used to quantify the performance of different orientation estimation algorithms . For the computation of rotation sequences and tilt angles a gimbal apparatus helps to avoid gold standard measurements (e.g. by camera-based systems). This is possible if the exact trajectory of the IMU sensor during the movement is known a priori. Therefore, as part of the thesis an appropriate gimbal will be constructed, which will help to put the IMU into defined static positions or run predefined rotation sequences. From a technical and scientific standpoint, the tool should be usable for the development, comparison and validation of algorithms that allow the computation of IMU orientation and rotation angles. For the scope of the thesis the following research question will be addressed: Is it possible to provide a simple and low-cost, but still valid evaluation of IMU orientation estimation algorithms using a gimbal?
- Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., & Havinga, P. (2010). “Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey.” In 23th International conference on architecture of computing systems 2010 (pp. 1-10). VDE.
- Maurer, M. W., Zrenner, M., Reynolds, D., Dümler, B., & Eskofier, B. M. (2018, March). “Sleeve based knee angle calculation for rehabilitation.” In 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (pp. 1-4). IEEE
- Manon Kok, Jeroen D. Hol and Thomas B. Schön (2017). “Using Inertial Sensors for Position and Orientation Estimation.” Foundations and Trends on Signal Processing, Vol. 11, No. 1-2: 1-153.
- Kim, A., & Golnaraghi, M. F. (2004). “A quaternion-based orientation estimation algorithm using an inertial measurement unit.” In PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No. 04CH37556) (pp. 268-272). IEEE.
- Seel, T., Raisch, J., & Schauer, T. (2014). “IMU-based joint angle measurement for gait analysis.” Sensors, Vol.14, No.4: 6891-6909.
- Brennan, A., Zhang, J., Deluzio, K., & Li, Q. (2011). “Quantification of inertial sensor-based 3D joint angle measurement accuracy using an instrumented gimbal.” Gait & posture, Vol.34, No.3:320-323.