The　surface　Electromyography　(sEMG)　signal　contains　information　about　movement　intention　generated　by　the　human　brain,　and　it　is　the　most　intuitive　and　common　solution　to　control　robots,　orthotics,　prosthetics　and　rehabilitation　equipment.　In　recent　years,　gesture　decoding　based　on　sEMG　signals　has　received　a　lot　of　research　attention.　In　this　paper,　the　effects　of　muscle　fatigue,　forearm　angle　and　acquisition　time　on　the　accuracy　of　gesture　decoding　were　researched.　Taking　11　static　gestures　as　samples,　four　specific　muscles　(i.e.,　superficial　flexor　digitorum　(SFD),　flexor　carpi　ulnaris　(FCU),　extensor　carpi　radialis　longus　(ECRL)　and　finger　extensor　(FE))　were　selected　to　sample　sEMG　signals.　Root　Mean　Square　(RMS),　Waveform　Length　(WL),　Zero　Crossing　(ZC)　and　Slope　Sign　Change　(SSC)　were　chosen　as　signal　eigenvalues;　Linear　Discriminant　Analysis　(LDA)　and　Probabilistic　Neural　Network　(PNN)　were　used　to　construct　classification　models,　and　finally,　the　decoding　accuracies　of　the　classification　models　were　obtained　under　different　influencing　elements.　The　experimental　results　showed　that　the　decoding　accuracy　of　the　classification　model　decreased　by　an　average　of　7%,　10%,　and　13%　considering　muscle　fatigue,　forearm　angle　and　acquisition　time,　respectively.　Furthermore,　the　acquisition　time　had　the　biggest　impact　on　decoding　accuracy,　with　a　maximum　reduction　of　nearly　20%.