Human movement is produced resulting from syner- getic combinations of multiple muscle contractions. The resultant joint movement can be estimated through the related multiple- muscle activities, which is formulated as the forward problem. Neuroprosthetic applications may benefit from co-contraction of agonist and antagonist muscle pairs to achieve more stable and robust joint movements. It is necessary to estimate the activations of each individual muscle from desired joint torque(s), which is the inverse problem. A synergy based solution is presented for the inverse estimation of multiple muscle activations from joint move- ment, focusing on one degree-of-freedom tasks. The approach comprises muscle synergy extraction via the non-negative matrix factorization algorithm. Cross validation is performed to evaluate the method for prediction accuracy based on experimental data from ten able-bodied subjects. The results demonstrate that the approach succeeds to inversely estimate the multiple muscle activities from the given joint torque sequence. In addition, the other one's averaged synergy ratio was applied for muscle activation estimation with leave-one-out cross validation manner, which resulted in 9.3% estimation error over all the subjects. The obtained results support the common muscle synergy based neuroprosthetics control concept.