Accidental　falls　pose　a　significant　threat　to　the　elderly　population,　and　accurate　fall　detection　from　surveillance　videos　can　significantly　reduce　the　negative　impact　of　falls.　Although　most　fall　detection　algorithms　based　on　video　deep　learning　focus　on　training　and　detecting　human　posture　or　key　points　in　pictures　or　videos,　we　have　found　that　the　human　pose-based　model　and　key　points-based　model　can　complement　each　other　to　improve　fall　detection　accuracy.　In　this　paper,　we　propose　a　preposed　attention　capture　mechanism　for　images　that　will　be　fed　into　the　training　network,　and　a　fall　detection　model　based　on　this　mechanism.　We　accomplish　this　by　fusing　the　human　dynamic　key　point　information　with　the　original　human　posture　image.　We　first　propose　the　concept　of　dynamic　key　points　to　account　for　incomplete　pose　key　point　information　in　the　fall　state.　We　then　introduce　an　attention　expectation　that　predicates　the　original　attention　mechanism　of　the　depth　model　by　automatically　labeling　dynamic　key　points.　Finally,　the　depth　model　trained　with　human　dynamic　key　points　is　used　to　correct　the　detection　errors　of　the　depth　model　with　raw　human　pose　images.　Our　experiments　on　the　Fall　Detection　Dataset　and　the　UP-Fall　Detection　Dataset　demonstrate　that　our　proposed　fall　detection　algorithm　can　effectively　improve　the　accuracy　of　fall　detection　and　provide　better　support　for　elderly　care.　©2023　the　Author(s),　licensee　AIMS　Press.