With　increasing　work　pressure　in　modern　society,　prolonged　sedentary　positions　with　poor　sitting　postures　can　cause　physical　and　psychological　problems,　including　obesity,　muscular　disorders,　and　myopia.　In　this　paper,　we　present　a　self-powered　sitting　position　monitoring　vest　(SPMV)　based　on　triboelectric　nanogenerators　(TENGs)　to　achieve　accurate　real-time　posture　recognition　through　an　integrated　machine　learning　algorithm.　The　SPMV　achieves　high　sensitivity　(0.16　mV/Pa),　favorable　stretchability　(10%),　good　stability　(12,000　cycles),　and　machine　washability　(10　h)　by　employing　knitted　double　threads　interlaced　with　conductive　fiber　and　nylon　yarn.　Utilizing　a　knitted　structure　and　sensor　arrays　that　are　stitched　into　different　parts　of　the　clothing,　the　SPMV　offers　a　non-invasive　method　of　recognizing　different　sitting　postures,　providing　feedback,　and　warning　users　while　enhancing　long-term　wearing　comfortability.　It　achieves　a　posture　recognition　accuracy　of　96.6%　using　the　random　forest　classifier,　which　is　higher　than　the　logistic　regression　(95.5%)　and　decision　tree　(94.3%)　classifiers.　The　TENG-based　SPMV　offers　a　reliable　solution　in　the　healthcare　system　for　non-invasive　and　long-term　monitoring,　promoting　the　development　of　triboelectric-based　wearable　electronics.