The estimate of joint angles, velocities, and accelerations is a key component of biomechanical modelling. The literature presents a variety of sensing modalities and algorithms to recover the full joint state, with tuning parameters varying between different applications, actions, and limbs. Comparisons between these methods are frequently limited to angles only, without comparison between the joint velocities and accelerations. This paper introduces an algorithm to fuse motion-capture and inertial measurements to recover the full state during a sit-to-stand task. This algorithm is then compared to three other methods: Kalman filtering on motion-capture or inertial measurements alone and the standard angular recovery/differentiation method. It is shown that the fusion of both optical and inertial measurements reduce the ripple and offset artefacts which become pronounced in high acceleration human motions.