Joint　torque　estimation　is　of　great　significance　for　the　research　and　clinical　application　of　intelligent　rehabilitation　technology.　This　paper　proposes　a　closed-loop　model　for　joint　torque　estimation　based　on　surface　electromyography　(sEMG).　Combined　with　the　physiological　characteristics　of　muscle　activation,　a　nonlinear　autoregressive　with　eXogenous　inputs(NARX)　neural　network　model　for　joint　torque　estimation　based　on　sEMG　signals　is　established.　In　order　to　solve　the　drift　phenomenon　of　torque　estimation,　a　state-space　framework　is　constructed　by　regarding　NARX　neural　network　based　torque　estimation　model　as　state　model　and　developing　a　measurement　model　by　the　easily　measured　joint　angle　signals.　With　the　built　state-space　model,　the　extended　Kalman　filter　(EKF)　is　used　to　realize　the　closed-loop　filtering　of　the　estimated　torque.　In　order　to　test　the　accuracy　of　the　proposed　closed-loop　joint　torque　estimation,　8　volunteers　were　recruited　to　perform　elbow　joint　isotonic　motion　experiments　under　four　kinds　of　loads.　The　test　results　show　that　the　average　normalized　root　mean　square　error　(NRMSE)　between　the　estimated　values　of　closed-loop　model　and　measurement　values　of　all　subjects　under　load-dependent,　multi-load　and　load-independent　tests　are　0.1080　+/-　0.0411,　0.1326　+/-　0.0494　and　0.1674　+/-　0.0661　respectively,　which　is　significant　better　than　the　results　of　the　open-loop　model　(0.2694　+/-　0.1584　(p　＜　10(-6)),　0.2499　+/-　0.1326　(p　＜　10(-6))　and　0.3435　+/-　0.2061　(p　＜　10(-4))).　The　presented　closed-loop　model　combines　offline　modeling　and　online　filtering　to　achieve　online　estimation　of　joint　torque,　which　ameliorates　the　problem　that　the　estimated　torque　in　the　open-loop　model　deviates　greatly　from　the　actual　values　and　improves　the　accuracy　of　joint　torque　estimation.