The　joint　torque　prediction　plays　an　important　role　in　rehabilitation　medicine,　clinical　medicine,　sports　training　and　other　fields.　The　continuous　and　real-time　torque　prediction　can　make　the　human-computer　interaction　equipment　better　feedback　and　reproduce　the　intention　of　human　motion.　To　provide　a　safe,　active　and　comfortable　rehabilitation　training　environment　for　patients　and　enhance　the　compliance　of　the　human-computer　interaction　equipment,　a　novel　method　of　joint　torque　prediction　is　proposed,　which　is　based　on　an　improved　recursive　cerebellar　model　neural　network.　In　this　method,　muscle　synergy　analysis　is　used　to　reduce　the　dimensionality　of　surface　electromyographic　(sEMG)　signals.　Then,　the　reduced-dimension　sEMG　feature　vector,　joint　angular　velocity　and　joint　angle　are　used　as　the　input　data　of　the　prediction　model.　In　addition,　recursive　unit　and　fuzzy　logic　rules　are　introduced　into　the　cerebellar　model　neural　network,　while　the　wavelet　function　is　used　as　membership　function.　Hence,　the　generalization　ability　of　the　network　is　optimized.　The　RWFCMNN　model　realizes　the　time　series　prediction　of　the　dynamic　torque　of　ankle　dorsiflexion　and　plantarflexion　in　three　states,　non-fatigue,　transitional　fatigue　and　fatigue.　The　average　Pearson　correlation　coefficient　and　the　average　normalized　root　mean　square　error　between　the　predicted　torque　and　the　actual　torque　are　0.933　5　and　0.159　8,　respectively.　These　numerical　values　verify　the　accuracy　and　effectiveness　of　this　method　for　continuous　prediction　of　lower　limb　joint　torque.　©　2022,　Science　Press.　All　right　reserved.