Falls　are　one　of　the　main　health　risks　among　the　elderly.　A　fall　detection　system　based　on　inertial　sensors　can　automatically　detect　fall　event　and　alert　a　caregiver　for　immediate　assistance,　so　as　to　reduce　injuries　causing　by　falls.　Nevertheless,　most　inertial　sensor-based　fall　detection　technologies　have　focused　on　the　accuracy　of　detection　while　neglecting　quantization　noise　caused　by　inertial　sensor.　In　this　paper,　an　activity　model　based　on　tri-axial　acceleration　and　gyroscope　is　proposed,　and　the　difference　between　activities　of　daily　living　(ADLs)　and　falls　is　analyzed.　Meanwhile,　a　Kalman　filter　is　proposed　to　preprocess　the　raw　data　so　as　to　reduce　noise.　A　sliding　window　and　Bayes　network　classifier　are　introduced　to　develop　a　wearable　fall　detection　system,　which　is　composed　of　a　wearable　motion　sensor　and　a　smart　phone.　The　experiment　shows　that　the　proposed　system　distinguishes　simulated　falls　from　ADLs　with　a　high　accuracy　of　95.67%,　while　sensitivity　and　specificity　are　99.0%　and　95.0%,　respectively.　Furthermore,　the　smart　phone　can　issue　an　alarm　to　caregivers　so　as　to　provide　timely　and　accurate　help　for　the　elderly,　as　soon　as　the　system　detects　a　fall.