As　we　all　know,　population　aging　is　a　significant　challenge　faced　by　Chinese　society,　and　ensuring　the　health　and　safety　of　elderly　individuals　has　become　an　urgent　topic　of　concern.　Within　the　context　of　family　safety,　elderly　individuals　often　experience　falls　or　fainting　due　to　age-related　physical　decline　or　underlying　medical　conditions.　In　response　to　this　phenomenon,　this　paper　presents　a　method　for　enhancing　the　wellbeing　of　family　members　by　utilizing　the　YOLOv5　model　to　detect　falls.　Moreover,　due　to　the　built-in　capability　of　YOLOv5　to　read　video　from　webcam,　this　technology　can　also　be　integrated　into　loT　devices,　turning　these　devices　into　a　part　of　smart　homes.　Considering　the　specific　nature　of　the　home　environment,　CAU　CAFall　is　considered　to　be　the　most　suitable　dataset.　Various　variations　of　the　YOLOv5　model　are　experi-mented　on　a　CAUCAFall　dataset　and　achieve　promising　results.　The　YOLOv5x　model　achieved　a　precision　of　82.2%,　while　the　YOLOv5s　model,　with　improved　running　speed,　achieved　an　precision　of　79.6%.　Finally,　we　explored　and　selected　the　most　suitable　YOLOv5　model　for　home　fall　detection　considering　comprehensive　evaluation　metrics,　and　it　is　YOLOv5s.　©　2023　IEEE.