Fabrics　are　considered　to　be　promising　alternatives　to　silicon　wafers　in　wearable　electronics,　and　the　strain　sensors　developed　based　on　them　have　attracted　widespread　attentions　for　their　promising　applications　in　health　and　motion　monitoring,　soft　robotics　and　human-computer　interaction,　etc.　However,　existing　fabric-based　strain　sensors　(FSS)　are　usually　prepared　by　complicated　methods　which　greatly　limit　their　practical　applicability.　Herein,　high-performance　FSSs　were　prepared　by　transferring　graphene　nanosheets　(GNSs)/multi-walled　carbon　nanotubes　(MWCNTs)　mixing　ink　onto　stretchable　cloth　adhesive　tape　via　a　simple　one-step　screen-printing　method.　The　sensing　range　and　sensitivity　of　the　prepared　FSSs　were　adjustable　by　designing　different　sensing　patterns,　and　the　FSS　with　a　high　GF　(40)　and　a　suitable　strain　range　(30%)　was　used　for　detection　of　finger　bending　angles.　The　optimized　FSS　features　excellent　linearity　(0.99),　outstanding　durability　(＞5000),　superior　breathability,　good　adhesion　and　ease　of　storage　and　use.　Combined　with　morphological　characterization　and　tensile　tests,　the　sensing　principle　of　the　FSS　was　explained.　Furthermore,　a　FSS-based　sensing　glove　was　inte-grated,　and　combined　with　LSTM　deep　learning　model;　it　achieves　a　high　accuracy　(95%)　of　dynamic　gesture　recognition　and　can　be　used　to　control　manipulator　which　demonstrates　its　promising　applications　in　the　field　of　smart　wearable　electronics　and　human-computer　interaction.