In this study, seven-channel electromyography signal-based two-dimensional wrist joint movement estimation with and without handgrip motions was carried out. Electromyography signals were analyzed using the synergy-based linear regression model and musculoskeletal model; they were subsequently compared with respect to single and combined wrist joint movements and handgrip. Using each one of wrist motion and grip trial as a training set, the synergy-based linear regression model exhibited a statistically significant performance with 0.7891 [+ or -] 0.0844 Pearson correlation coefficient (r) value in two- dimensional wrist motion estimation compared with 0.7608 [+ or -] 0.1037 r value of the musculoskeletal model. Estimates on the grip force produced 0.8463 [+ or -] 0.0503 r value with 0.2559 [+ or -] 0.1397 normalized root-mean-square error of the wrist motion range. This continuous wrist and handgrip estimation can be considered when electromyography-based multi-dimensional input signals in the prosthesis, virtual interface, and rehabilitation are needed.