Accidental　falls　often　cause　serious　harm　to　the　human　body,　especially　for　the　elderly.　But　falls　tend　to　be　infrequent,　making　it　difficult　to　collect　large　amounts　of　data　for　research.　In　reality,　there　is　a　large　gap　between　the　amount　of　sensor　data　collected　by　falling　activities　and　daily　activities,　which　will　lead　to　class　imbalance.　When　using　machine　learning　algorithms　to　detect　falls,　class　imbalance　will　cause　the　performance　of　the　classifier　to　be　biased　towards　most　classes　and　reduce　the　detection　accuracy　of　a　few　classes.　When　faced　with　the　problem　of　binary　class　imbalance,　selecting　an　effective　machine　learning　algorithm　and　resampling　data　can　effectively　improve　the　accuracy　of　classification.　In　this　paper,　an　ensemble　learning　algorithm　and　clustering　undersampling　method　are　used　for　fall　detection.　The　ensemble　learning　algorithm　can　reduce　the　impact　of　imbalanced　datasets　on　the　training　model　through　multiple　classifier　iterations.　Clustering　undersampling　method　can　change　the　dataset　distribution　and　balance　the　number　of　positive　and　negative　samples.　The　method　in　this　paper　is　evaluated　on　the　public　dataset　Sisfall.　Compared　with　the　traditional　machine　learning　algorithms,　the　ensemble　learning　has　higher　accuracy　and　faster　training　speed.　Combined　with　the　clustering　undersampling　method,　the　method　has　a　higher　recall　and　precision.　©　2022　IEEE.