Evaluation of Inertial Sensor Fusion Algorithms in Grasping Tasks Using Real Input Data: Comparison of Computational Costs and Root Mean Square Error

Sensor fusion is an important computation step for acquiring reliable orientation information from inertial sensors. These sensors are very attractive in order to achieve a mobile capturing of human movements, which is desired for application in sports or rehabilitation. Commercial inertial sensors with small form factors and low power consumption can be used for capturing without any interference. There are several common techniques for calculating orientation data based on RAW sensor data. This paper gives an overview of the computational effort and achievable accuracy of integration algorithms, vector observation algorithms and Kalman filter algorithms for inertial sensor fusion. The sensor data were compared against an optical motion capturing system. The considered application is the capturing of arm movements during grasping tasks in stroke rehabilitation. Therefore, the algorithms are evaluated based on corresponding real world input data. The provided benchmark compares the sensor fusion algorithms in terms of computational cost and orientation estimation error.