Background: Traditional myoelectric controls of prostheses for transhumeral amputees fail to provide intuitive coordination of the necessary degrees of freedom. Building upon promising advances in movement-based controls and computer vision, we have previously demonstrated that reconstructing the distal joints based on Artificial Neural Network (ANN) predictions, while knowing the shoulder posture and the movement goal (i.e., position and orientation of the targeted object), enables participants to position and orient an avatar hand to grasp objects scattered throughout a wide workspace with performances comparable to that of a natural arm. However, this previous control involved rapid and unintended prosthesis movements that resulted from sudden changes in the ANN predictions at each modification of the movement goal, rendering its use impractical for real-life scenarios. Methods: Here, we designed and tested novel methods to eliminate this abrupt change, based on an angular trajectory, determined from the speed of stump movement and the gap remaining between the current and the ’goal’ distal configurations. Two methods are presented to define this ’goal’ configuration, either relying solely on the movement goal or also taking into account the current shoulder posture. These new controls are tested offline and online (i.e., involving participants-in-the-loop) and compared to performances obtained with a Natural control as with our previously proposed control (i.e., without angular trajectory). Results: Despite a slight increase in movement time, potentially due to the increased trajectory curvature observed offline, the new controls allowed twelve non-disabled participants and six participants with transhumeral limb loss to reach objects at various positions and orientations in a virtual reality set-up without prior training. Furthermore, while valid participants rated the usability lower and the workload higher with our controls compared to the Natural control,  participants with upper limb disabilities did not, indicating their genuine interest in our novel controls. Conclusions: The good performances achieved with those controls, particularly when the current shoulder posture was accounted for, represent a necessary step towards applications in real-world scenarios. Notably, the good ratings by participants with upper limb disability highlight the potential acceptability and effectiveness of those controls for our target population.