The　existing　Human-Machine　Interfaces　(HMI)　based　on　gesture　recognition　using　surface　electromyography　(sEMG)　have　made　significant　progress.　However,　the　sEMG　has　inherent　limitations　as　well　as　the　gesture　classification　and　force　estimation　have　not　been　effectively　combined.　There　are　limitations　in　applications　such　as　prosthetic　control　and　clinical　rehabilitation,　etc.　In　this　paper,　a　grasping　gesture　and　force　recognition　strategy　based　on　wearable　A-mode　ultrasound　and　two-stage　cascade　model　is　proposed,　which　can　simultaneously　estimate　the　force　while　classifying　the　grasping　gesture.　This　paper　experiments　five　grasping　gestures　and　four　force　levels　(5-50%MVC).　The　results　demonstrate　that　the　performance　of　the　proposed　model　is　significantly　better　than　that　of　the　traditional　model　both　in　classification　and　regression　(p　＜　0.001).　Additionally,　the　two-stage　cascade　regression　model　(TSCRM)　used　the　Gaussian　Process　regression　model　(GPR)　with　the　mean　and　standard　deviation　(MSD)　feature　obtains　excellent　results,　with　normalized　root-mean-square　error　(nRMSE)　and　correlation　coefficient　(CC)　of　0.10490.0374　and　0.94610.0354,　respectively.　Besides,　the　latency　of　the　model　meets　the　requirement　of　real-time　recognition　(T　＜　15ms).　Therefore,　the　research　outcomes　prove　the　feasibility　of　the　proposed　recognition　strategy　and　provide　a　reference　for　the　field　of　prosthetic　control,　etc.