This　paper　proposes　a　novel　time-frequency　feature　fusion　method　to　recognise　patients＇　behaviours　based　on　the　Frequency　Modulated　Continuous　Wave　(FMCW)　radar　system,　which　can　locate　patients　as　well　as　recognise　their　current　actions　and　thus　is　expected　to　solve　the　shortage　of　medical　staff　caused　by　the　novel　coronavirus　pneumonia　(COVID-19).　To　recognise　the　patient＇s　behaviour,　the　FMCW　radar　is　utilised　to　acquire　point　clouds　reflected　by　the　human　body,　and　the　micro-Doppler　spectrogram　is　generated　by　human　motion.　Then　features　are　extracted　and　fused　from　the　time-domain　information　of　point　clouds　and　the　frequency-domain　information　of　the　micro-Doppler　spectrogram　respectively.　According　to　the　fused　features,　the　patient＇s　behaviour　is　recognised　by　a　Bayesian　optimisation　random　forest　algorithm,　where　the　role　of　Bayesian　optimisation　is　to　select　the　best　hyper-parameters　for　the　random　forest,　i.e.　the　number　of　random　forest　decision　trees,　the　depth　of　leaves,　and　the　number　of　features.　The　experimental　results　show　that　an　average　accuracy　of　99.3%　can　be　achieved　by　using　the　time-frequency　fusion　with　the　Bayesian　optimisation　random　forest　model　to　recognise　six　actions.　©　2022　IOP　Publishing　Ltd.