Introduction:　Human　joint　moment　is　a　critical　parameter　to　rehabilitation　assessment　and　human-robot　interaction,　which　can　be　predicted　using　an　artificial　neural　network　(ANN)　model.　However,　challenge　remains　as　lack　of　an　effective　approach　to　determining　the　input　variables　for　the　ANN　model　in　joint　moment　prediction,　which　determines　the　number　of　input　sensors　and　the　complexity　of　prediction.　Methods:　To　address　this　research　gap,　this　study　develops　a　mathematical　model　based　on　the　Hill　muscle　model　to　determining　the　online　input　variables　of　the　ANN　for　the　prediction　of　joint　moments.　In　this　method,　the　muscle　activation,　muscle-tendon　moment　velocity　and　length　in　the　Hill　muscle　model　and　muscle-tendon　moment　arm　are　translated　to　the　online　measurable　variables,　i.e.,　muscle　electromyography　(EMG),　joint　angles　and　angular　velocities　of　the　muscle　span.　To　test　the　predictive　ability　of　these　input　variables,　an　ANN　model　is　designed　and　trained　to　predict　joint　moments.　The　ANN　model　with　the　online　measurable　input　variables　is　tested　on　the　experimental　data　collected　from　ten　healthy　subjects　running　with　the　speeds　of　2,　3,　4　and　5　m/s　on　a　treadmill.　The　variance　accounted　for　(VAF)　between　the　predicted　and　inverse　dynamics　moment　is　used　to　evaluate　the　prediction　accuracy.　Results:　The　results　suggested　that　the　method　can　predict　joint　moments　with　a　higher　accuracy　(mean　VAF　=　89.67　+/-　5.56　%)　than　those　obtained　by　using　other　joint　angles　and　angular　velocities　as　inputs　(mean　VAF　=　86.27　+/-　6.6%)　evaluated　by　jack-knife　cross-validation.　Conclusions:　The　proposed　method　provides　us　with　a　powerful　tool　to　predict　joint　moment　based　on　online　measurable　variables,　which　establishes　the　theoretical　basis　for　optimizing　the　input　sensors　and　detection　complexity　of　the　prediction　system.　It　may　facilitate　the　research　on　exoskeleton　robot　control　and　real-time　gait　analysis　in　motor　rehabilitation.