A novel 7 degrees of freedom model for upper limb kinematic reconstruction based on wearable sensors

Abstract

Wearable motion tracking systems have gained large popularity in the last decades because of their effectiveness in many fields, from performance assessment to human-robot interaction. Among all the approaches, those based on inertial sensors have been widely explored. Since inertial sensors are affected by measurements drift, they need to be aided by other sensors, thus requiring sensor measurements to be fused. The most used sensor fusion techniques are based on Kalman filter. In particular, the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are used because of the non linearity characterizing most of the models. They often aim at reconstructing human motion by estimating limbs orientation, involving human's kinematics to constrain relative motion of the limbs. These models often neglect part of the degrees of freedom (DoFs) that characterize human upper limbs, especially when modeling humerus motion with respect to the chest. In this paper we present a novel 7 DoFs model which represents a trade-off between modeling accuracy and complexity for the human upper limb. In particular, we model the human shoulder girdle taking into account also the humerus head's elevation and the retraction due to the scapula's and the clavicle's motions. The model exploits inertial sensors measurements by means of an Unscented Kalman filter to reconstruct human movements. The system performance is validated firstly against a reconstruction based on an optical tracking system. Secondly, the 5 DoFs model extracted form the 7 DoFs one was checked to have state of the art performance and used to estimate the improvement of position estimation that are obtained by extending the model to 7 DoFs.