The presented work develops a high-dynamic quaternion attitude estimation Unscented Kalman Filter suitable for the implementation in low cost sensor systems, comprising accelerometers, gyroscopes and magnetic field sensors. The adoption of a spherical σ-point selection strategy and the implementation of the square-root version reduces the requirements for computational resources. A special handling of angular rate, gyroscope bias, and translational accelerations within the process model is implemented and evaluated. Issues regarding the quaternion mean calculations, noise representation as well as control noise scaling are discussed. The performance of the designed filter is assessed using real hand motion reference data and correspondingly generated noisy sensor measurements.