Traditional　muscle　strength　training　instruments　often　rely　on　torque-based　feedback　to　guide　exercises,　which　can　introduce　delays　in　system　response　and　result　in　discomfort　due　to　hysteresis　effects.　Surface　electromyography　(sEMG)　signals　were　used　as　control　inputs　to　overcome　the　lag　in　torque-based　muscle　strength　training　instruments.　The　sEMG　is　generated　20–80　ms　before　movement,　which　is　called　＇muscle　electromechanical　delay.＇　If　the　torque　can　be　effectively　predicted　during　this　period,　the　lag　effect　can　be　significantly　reduced,　thus　improving　the　effectiveness　and　comfort　of　training.　We,　therefore,　proposed　a　multistep　ahead　(MSA)　model　based　on　the　nonlinear　autoregressive　network　with　exogenous　inputs　(NARX)　dynamic　recurrent　neural　network.　It　predicted　torques　using　sEMG,　and　allowed　natural　control　of　the　instrument.　The　results　showed　that　the　normalized　root-mean-square　error　(NRMSE)　was　lower　than　0.1167,　and　the　Pearson　correlation　coefficients　(ρ)　exceeded　0.9444,　even　when　the　ahead　steps　achieved　35.　The　intrasubject　and　the　intersubject　validation　demonstrated　significantly　lower　NRMSE　(　p　©　2025　IEEE.　All　rights　reserved.