Heart　rate　variability　(HRV),　indicating　the　variation　in　intervals　between　consecutive　heartbeats,　is　a　crucial　physiological　indicator　of　human　health.　However,　detecting　HRV　using　frequency-modulated　continuous-wave　(FMCW)　radar　is　highly　susceptible　to　interference　from　respiration,　minor　body　movements,　and　environmental　noise,　especially　in　multitarget　scenarios.　To　address　these　challenges,　we　propose　the　Health-Radar　system,　which　comprises　three　functional　modules.　In　the　target　detection　module,　the　system　accurately　identifies　the　number　and　locations　of　targets.　In　the　phase　extraction　module,　the　signal　undergoes　dc　offset　calibration　to　extract　the　chest　displacement　signals.　In　the　heartbeat　signal　extraction　module,　we　introduce　Health-VMD,　an　adaptive　parameter　variational　mode　decomposition　(VMD)　method.　This　method　optimizes　the　VMD　parameters　using　an　improved　grasshopper　optimization　algorithm　(GOA)　and　accurately　extracts　vital　sign　signals　from　chest　displacement　signals　to　estimate　HRV.　In　addition,　we　propose　a　novel　objective　function,　composed　of　permutation　entropy,　mutual　information,　and　energy　loss　rate　(PME),　specifically　designed　for　vital　sign　extraction.　Experiments　with　multiple　participants　in　various　scenarios　demonstrated　that　the　designed　system　can　accurately　identify　different　targets　and　detect　HRV　with　high　precision.　The　root-mean-square　error　(RMSE)　of　the　detected　interbeat　intervals　(IBIs)　is　29.72　ms,　the　RMSE　of　the　standard　deviation　of　NN　intervals　(SDNN)　is　4.1　ms,　and　the　RMSE　of　the　root　mean　square　of　successive　differences　(RMSSD)　is　18.61　ms,　outperforming　existing　methods.