Adapting　to　the　limited　resource　of　mobile　device,　a　human　state　recognition　algorithm　based　on　Kalman　filter　was　proposed,　which　could　identify　dynamic,　static　and　state　transition　in　real　time.　The　Bluetooth　module　with　a　triaxial　accelerometer　was　placed　on　the　chest　of　body　to　collect　three-dimensional　acceleration　data.　The　characteristic　of　human　activity　was　associated　with　the　features　of　the　accelerometer　signal,　so　the　function　of　change　of　the　signal　vector　magnitude　(SVM)　was　processed　by　Kalman　filter　to　identify　human　state.　Experiment　results　show　that　the　algorithm　achieves　high　accuracy　in　identification　of　postural　transition,　meanwhile,　the　algorithm　has　displayed　better　performance　with　little　overhead　on　the　smartphone.