Upper limb impairment is a loss of motor function after a stroke, leading to difficulties in performing daily tasks. With a low remission rate six months after stroke, monitoring during this critical period is essential. Telemonitoring with inertial sensors has become a common approach that requires the identification and recognition of specific movements. However, data quality in clinical databases is a challenge, making data augmentation necessary. In this work, we propose a novel method that can learn a trajectory subspace from partial 3D signals in an unsupervised manner. Our method is simple yet effective, producing novel human-feasible motions compatible with the estimated trajectory subspace. To this end, three approaches are introduced from global to local models that can capture a wide variety of human motions. Our method outperforms the results in the state of the art in both control and patient subjects.