The　wearable　A-mode　ultrasound　human-machine　interface　technology　(HMI-A)　is　a　promising　sensing　modality,　with　many　researchers　achieving　good　results　in　strictly　controlled　experimental　environments.　However,　the　instability　of　A-mode　ultrasound　signals　makes　gesture　recognition　technology　associated　with　HMI-A　difficult　to　apply　in　practical　scenarios,　and　the　anatomical　variability　of　the　forearm　is　a　major　factor　contributing　to　the　decrease　in　gesture　recognition　performance.　Additionally,　long-term　application　can　lead　to　forearm　posture　changes　and　probe　displacement,　causing　signal　drift.　If　the　distribution　of　signal　data　between　the　training　set　and　the　test　set　is　inconsistent,　the　performance　of　the　trained　model　on　the　test　set　will　be　poor.　Addressing　the　above　issues,　this　article　makes　three　contributions:　1)　a　thorough　investigation　of　forearm　posture　changes,　including　pronation,　supination,　flexion,　and　extension,　and　their　impact　on　HMI-A　gesture　recognition　performance;　2)　proposing　an　unmarked　calibration　algorithm　based　on　quantitative　analysis　to　help　users　reposition　the　forearm　after　long-term　use;　and　3)　introducing　a　domain-adversarial　neural　network　(DANN)　to　mitigate　the　impact　of　signal　drift　on　recognition　performance.　Through　five　interval　experiments　with　eight　subjects,　the　long-term　gesture　recognition　performance　of　the　combination　of　repositioning　and　DANN　methods　was　validated.　The　average　recognition　accuracy　(RA)　of　each　experiment　increased　from　58.81%　+/-　3.61%　to　89.17%　+/-　1.72%,　with　one　subject＇s　RA　improving　by　60.2%.　This　study　confirms　the　feasibility　of　using　ultrasound　sensing　technology　for　long-term　muscle　tissue-related　applications.