As　the　most　basic　and　important　sense　of　the　human　body,　kinesthesia　has　the　ability　to　act　and　adapt　to　stimuli.　Simulating　the　kinesthetic　process　from　the　level　of　sensory　neurons　is　an　important　task　toward　the　emulation　of　neuromorphic　computation,　while　currently　report　for　artificial　kinesthetic　system　is　still　not　available.　Hence,　in　this　work,　an　artificial　kinesthetic　system　is　developed　which　consists　of　a　single-electrode　triboelectric　nanogenerator　(S-TENG)　that　can　be　attached　to　human　skin　and　a　field　effect　synaptic　transistor　(FEST).　The　S-TENG　based　on　PDMS/MXene　friction　layer　exhibits　high　sensitivity　of　0.197　kPa−1　in　a　low-pressure　region　(−1　in　a　high-pressure　region　(6–30　kPa).　FEST　achieves　synaptic　plasticity　in　biology　and　simulates　the　short-term　to　long-term　memory　transition　and　learning　process.　Artificial　kinesthetic　system　can　readily　achieve　the　perception　of　human　muscle/joint　motion　state　and　orientation　information.　In　addition,　the　assessment　of　fatigue　driving　risk　is　realized　which　substantially　improves　the　efficiency　and　accuracy　of　the　instruction　recognition　process.　Furthermore,　the　identification　of　ASL　(American　Sign　Language)　gestures　is　simulated　to　demonstrate　the　recognition　accuracy.　This　work　shows　a　widespread　potential　in　the　construction　of　next-generation　neuromorphic　sensory　network,　neurorobotics　and　interactive　artificial　intelligence.　©　2021　Elsevier　Ltd