Researchers　are　investigating　how　to　make　machines　read　our　body　language　to　make　human-machine　interaction　(HMI)　more　intelligent　and　efficient.　The　lower　limbs　contain　a　variety　of　gestures,　and　it　is　also　one　of　the　most　effective　ways　to　express　body　information.　Therefore,　foot　gesture　recognition　(FGR)　has　become　a　popular　technology　for　human-machine　interface　with　simple,　fast,　and　accurate　features.　To　give　the　reader　a　quick　overview　of　the　current　state　of　FGR　research,　this　review　takes　the　sensing　methods　used　in　the　FGR　technology　as　an　entry　point,　introduces　different　sensing　methods,　machine　learning　algorithms,　and　applications,　and　discusses　the　limitations　and　future　work　on　FGR　systems.　The　results　show　that　the　mainstream　sensing　methods　for　FGR　are　plantar　pressure,　inertial,　visual,　surface　electromyography　(sEMG),　and　ultrasound　(US).　Current　applications　of　FGR　are　simplified　control,　medical　rehabilitation,　virtual　reality　(VR),　and　smart　prosthetics.　Research　on　hybrid　sensing　methods　and　deep　learning　algorithms　has　gradually　increased　in　recent　years.　Future　research　will　focus　on　designing　sensor　hardware　that　can　respond　to　environmental　changes,　using　multimodal　sensing　for　interaction,　and　designing　more　comfortable　and　portable　FGR　systems.