Falls　have　been　a　major　health　risk　that　diminishes　the　quality　of　life　among　the　elderly.　In　this　paper,　we　propose　a　new　method　using　acceleration　data　and　hidden　Markov　model　(HMM)　to　detect　fall　events.　A　wearable　device　integrating　a　tri-axial　accelerometer　was　used　to　collect　acceleration　data　of　human　chest.　Feature　sequences　(FSs)　were　extracted　from　the　acceleration　data　and　used　as　sequence　of　observations　to　train　an　HMM　of　fall　detection.　The　probability　of　the　input　FS　generated　by　the　model　was　calculated　as　the　detection　standard.　Experimental　results　showed　that　the　accuracy　of　the　proposed　method　was　97.2%,　the　sensitivity　was　91.7%,　and　the　specificity　was　100%,　demonstrating　desired　performance　of　our　method　in　detecting　fall　events.　©　2016　IEEE.