The　Bluetooth-based　wearable　fall　detection　technology　faces　problems　such　as　short　transmission　distance,　easily　interfered　by　obstacles,　and　high-power　consumption.　To　address　these　issues,　we　developed　a　sensor　board　integrated　with　low-power　ZigBee　and　MPU6050,　which　can　sample　and　cache　three-axial　acceleration　and　angular　velocity　data　in　the　sleep　mode;　we　also　designed　an　interrupt-driven　algorithm　that　can　collect　and　transmit　the　data　to　the　receiving　end　(namely　server)　with　low-power　consumption　via　ZigBee.　Additionally,　the　received　data　are　normalized　according　to　the　range　specification　and　cached　into　a　sliding　window　by　the　server.　Meanwhile,　the　cached　data　are　mapped　into　RGB　bitmap,　and　a　fall　detection　convolutional　neural　network　(FD-CNN)　is　designed　and　trained　using　the　open　dataset　to　identify　falls　from　the　activities　of　daily　livings　according　to　bitmap.　The　experimental　results　show　that　the　average　accuracy　of　this　method　is　98.61%,　while　its　average　sensitivity　and　specificity　are　98.62%　and　99.80%,　respectively.　It　takes　advantage　of　the　strong　networking　capacity　of　ZigBee,　and　the　strong　computing　power　of　the　server,　which　is　very　suitable　for　fall　sensing　in　elderly　community　with　low　power　and　high　accuracy.