A-mode　ultrasound　has　the　advantages　of　high　resolution,　easy　calculation　and　low　cost　in　predicting　dexterous　gestures.　In　order　to　accelerate　the　popularization　of　A-mode　ultrasound　gesture　recognition　technology,　we　designed　a　human-machine　interface　that　can　interact　with　the　user　in　real-time.　Data　processing　includes　Gaussian　filtering,　feature　extraction　and　PCA　dimensionality　reduction.　The　NB,　LDA　and　SVM　algorithms　were　selected　to　train　machine　learning　models.　The　whole　process　was　written　in　C++　to　classify　gestures　in　real-time.　This　paper　conducts　offline　and　real-time　experiments　based　on　HMI-A　(Human-machine　interface　based　on　A-mode　ultrasound),　including　ten　subjects　and　ten　common　gestures.　To　demonstrate　the　effectiveness　of　HMI-A　and　avoid　accidental　interference,　the　offline　experiment　collected　ten　rounds　of　gestures　for　each　subject　for　ten-fold　cross-validation.　The　results　show　that　the　offline　recognition　accuracy　is　96.92%　+/-　1.92%.　The　real-time　experiment　was　evaluated　by　four　online　performance　metrics:　action　selection　time,　action　completion　time,　action　completion　rate　and　real-time　recognition　accuracy.　The　results　show　that　the　action　completion　rate　is　96.0%　+/-　3.6%,　and　the　real-time　recognition　accuracy　is　83.8%　+/-　6.9%.　This　study　verifies　the　great　potential　of　wearable　A-mode　ultrasound　technology,　and　provides　a　wider　range　of　application　scenarios　for　gesture　recognition.