Magnetic-Free Quaternion-Based Robust Unscented Kalman Filter for Upper Limb Kinematic Analysis

Human motion analysis is a field of increasing interest. In most applications, an accurate estimation of joint angles is needed. Inertial measurement units (IMUs) represent a very promising technology for motion capture. Unfortunately, their performances are strongly influenced by different sources of error (e.g., gyroscopes biases). To limit the effect of these sources of error, several algorithms (usually defined as 'sensor fusion' algorithms) are used. The proposed work aims to validate a magnetic-free quaternion-based robust unscented Kalman filter (UKF) for upper limb kinematic analysis (i.e., estimation of the articular joint angles), using an inertial body sensor network, in a set of complex 3-D movements (i.e., the yoga poses). The algorithm was tested on ten expert yoga practitioners during the execution of the sun salutation sequence. Joint angle estimations were compared with the ones obtained from an optoelectronic reference system by evaluating the root mean square errors (RMSEs) and Pearson's correlation coefficients. The achieved worst case was 8.41°, while the best one was 2.96° for RMSEs mean values. The accuracy of the algorithm was further confirmed by the high values of Pearson's correlation coefficients (lowest mean value of 0.80). In addition, the comparison with one of the most used filters for attitude estimation [extended Kalman filter (EKF)] showed no significant differences between the two methods. Up to authors knowledge, the scientific literature lacks the development of a complete magnetic-free version of the UKF for human motion capture and analysis in complex movements, such as the one introduced here. The proposed work aims at filling this gap.