Force　prediction　is　crucial　for　functional　rehabilitation　of　the　upper　limb.　Surface　electromyography　(sEMG)　signals　play　a　pivotal　role　in　muscle　force　studies,　but　its　non-stationarity　challenges　the　reliability　of　sEMG-driven　models.　This　problem　may　be　alleviated　by　fusion　with　electrical　impedance　myography　(EIM),　an　active　sensing　technique　incorporating　tissue　morphology　information.　This　study　designed　a　wearable　multimodal　physiological　measurement　system　to　acquire　sEMG　and　EIM　signals　simultaneously.　The　feature　quantification　indexes　were　defined　for　quantitative　analysis　of　the　efficacy　of　EIM　and　sEMG　in　static　force　prediction.　We　finally　proposed　Self-Attention　Convolutional　Long　Short-Term　Memory　(SACLSTM)　network　to　capture　the　spatio-temporal　information　among　EIM　and　sEMG　features　for　cross-modal　feature　fusion.　The　results　indicated　that　EIM　exhibited　greater　sensitivity　to　variations　in　static　force　compared　to　sEMG,　especially　at　low　muscle　activation　levels.　Furthermore,　the　proposed　SACLSTM　network　is　significantly　superior　to　LSTM,　ConvLSTM,　and　several　other　baseline　methods.　Compared　to　the　LSTM　and　ConvLSTM　networks,　the　SACLSTM　model　exhibits　an　R-2　improvement　of　12.4%　and　3%,　respectively,　and　an　root　mean　square　error　reduction　of　63%　and　29%.　Especially　for　patients　with　upper　limb　dysfunction,　the　accuracy　and　stability　of　the　multimodal　model　were　significantly　improved　after　feature　fusion　compared　with　using　only　EIM　or　sEMG　unimodal　features.　This　study　emphasised　the　great　potential　of　fusing　EIM　and　sEMG　features　to　improve　performance　in　the　muscle　force　prediction,　opening　up　new　practice　paths　in　the　field　of　functional　motor　rehabilitation.