the　activity　model　based　on　tri-axial　acceleration　and　gyroscope　is　proposed　in　this　paper,　and　the　difference　between　activities　of　daily　living　of　(ADLs)　and　falls　is　analyzed　at　first.　Meanwhile,　Kalman　filter　is　proposed　to　reduce　noise.　kNN　algorithm　and　slide　window　are　introduced　to　develop　a　wearable　system　for　fall　detection　and　alert,　which　is　composed　of　a　wearable　motion　sensor　and　a　smart　phone.　It　is　shown　by　experiment　that　the　system　identifies　simulated　falls　from　ADLs　with　a　high　accuracy　of　97.17%,　while　sensitivity　and　specificity　are　97.00%　and　97.50%,　respectively.　Moreover,　the　smart　phone　can　issue　an　alarm　to　caregivers　so　as　to　provide　timely　and　accurate　help　for　the　elderly,　as　soon　as　a　fall　is　detected.