Joint　moment　is　an　important　parameter　for　a　quantitative　assessment　of　human　motor　function.　However,　most　existing　joint　moment　prediction　methods　lacking　feature　selection　of　optimal　inputs　subset,　which　reduced　the　prediction　accuracy　and　output　comprehensibility,　increased　the　complexity　of　the　input　sensor　structure,　making　the　portable　prediction　equipment　impossible　to　achieve.　To　address　this　problem,　this　paper　develops　a　novel　method　based　on　the　binary　particle　swarm　optimization　(BPSO)　with　the　variance　accounted　for　(VAF)　as　fitness　function　to　reduce　the　number　of　input　variables　while　improves　the　accuracy　in　joint　moment　prediction.　The　proposed　method　is　tested　on　the　experimental　data　collected　from　ten　healthy　subjects　who　are　running　on　a　treadmill　with　four　different　speeds　of　2,　3,　4　and　5m/s.　The　BPSO　is　used　to　select　optimal　inputs　subset　from　ten　electromyography　(EMG)　data　and　six　joints　angles,　and　then　the　selected　optimal　inputs　subset　be　used　to　train　and　predict　the　joint　moments　via　artificial　neural　network　(ANN).　Prediction　accuracy　is　evaluated　by　the　variance　accounted　for　(VAF)　test　between　the　predicted　joint　moment　and　multi-body　dynamics　moment.　Results　show　that　the　proposed　method　can　reduce　the　number　of　input　variables　of　five　joint　moment　from　16　to　less　than　11.　Furthermore,　the　proposed　method　can　better　predict　joint　moment　(mean　VAF.　94.40　+/-　0　.84%)　in　comparison　with　the　state-of-the-art　methods,　i.e.　Elastic　Net　(mean　VAF.　93.38　+/-　0　.96%)　and　mutual　information　(mean　VAF.　86.27　+/-　1.41%).　In　conclusion,　the　proposed　method　reduces　the　number　of　input　variables　and　improves　the　prediction　accuracy　that　may　allow　the　future　development　of　a　portable,　non-invasive　system　for　joint　moment　prediction.　As　such,　it　may　facilitate　real-time　assessment　of　human　motor　function.