There　is　uncertainty　in　the　neuromusculoskeletal　system,　and　deterministic　models　cannot　describe　this　significant　presence　of　uncertainty,　affecting　the　accuracy　of　model　predictions.　In　this　paper,　a　knee　joint　angle　prediction　model　based　on　surface　electromyography　(sEMG)　signals　is　proposed.　To　address　the　instability　of　EMG　signals　and　the　uncertainty　of　the　neuromusculoskeletal　system,　a　non-parametric　probabilistic　model　is　developed　using　a　Gaussian　process　model　combined　with　the　physiological　properties　of　muscle　activation.　Since　the　neuromusculoskeletal　system　is　a　dynamic　system,　the　Gaussian　process　model　is　further　combined　with　a　non-linear　autoregressive　with　eXogenous　inputs　(NARX)　model　to　create　a　Gaussian　process　autoregression　model.　In　this　paper,　the　normalized　root　mean　square　error　(NRMSE)　and　the　correlation　coefficient　(CC)　are　compared　between　the　joint　angle　prediction　results　of　the　Gaussian　process　autoregressive　model　prediction　and　the　actual　joint　angle　under　three　test　scenarios:　speed-dependent,　multi-speed　and　speed-independent.　The　mean　of　NRMSE　and　the　mean　of　CC　for　all　test　scenarios　in　the　healthy　subjects　dataset　and　the　hemiplegic　patients　dataset　outperform　the　results　of　the　Gaussian　process　model,　with　significant　differences　(p　＜　0.05　and　p　＜　0.05,　p　＜　0.05　and　p　＜　0.05).　From　the　perspective　of　uncertainty,　a　non-parametric　probabilistic　model　for　joint　angle　prediction　is　established　by　using　Gaussian　process　autoregressive　model　to　achieve　accurate　prediction　of　human　movement.