Joint　torque　prediction　plays　an　important　role　in　quantitative　limb　rehabilitation　assessment　and　exoskeleton　robot,　and　it　is　essential　to　acquire　feedback　or　feedforward　signal　for　adaptive　functional　electrical　stimulation　(FES)　control.　The　Surface　electromyography　(sEMG)　signal　is　one　of　the　basic　processing　techniques　to　detect　muscle　activity,　and　also　one　favorable　technique　to　estimate　joint　torque.　In　order　to　predict　joint　torque　in　a　wide　range　of　real　time　convenient　applications,　it　is　necessary　to　fuse　sEMG　signals　with　other　convenient　physical　sensors　such　as　accelerometers　and　gyroscopes,　herein,　we　use　a　time　delay　artificial　neural　network　to　predict　human　joint　force　of　ankle　eversion　and　inversion　based　on　sEMG　and　angular　velocity　signals.　We　testify　our　method　on　the　data　recorded　from　8　subjects　(5　healthy　subjects　and　3　patients)　who　are　on　isokinetic　ankle　eversion　and　inversion.　The　results　show　that　the　mean　Cross-correlation　coefficients　(rho)　and　the　mean　normalized　root-mean-square　deviation　(NRMSE　%)　calculated　between　the　prediction　and　the　real　value　for　isokinetic　contraction　is　0.966　+/-　0.019　and　7.9%　+/-　0.026.　Compared　with　artificial　neural　network　(ANN)　and　support　vector　regression　(SVR),　the　proposed　method　can　predict　the　joint　torque　effectively.　For　the　future　application,　the　method　has　the　potential　to　be　employed　to　predict　the　ankle　moments　in　real-time　application　for　quantitative　lower　limb　rehabilitation　assessment　and　exoskeleton　robot　control.