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.