The　development　of　intelligent　rehabilitation　robots　has　greatly　reduced　the　workload　of　rehabilitation　physicians.　Human-machine　interaction　(HMI)　control　methods　are　a　critical　technology　for　intelligent　rehabilitation　robots.　Therefore,　we　systematically　review　the　HMI　methods　and　control　strategies　for　upper　and　lower　limb　rehabilitation　robots　and　summarizing　the　HMI　methods　with　different　sensors.　The　integration　of　rehabilitation　robots　and　HMI　control　methods　has　grown　significantly　in　recent　years.　For　this　reason,　this　article　takes　the　sensing　methods　as　the　entry　point　to　give　readers　a　quick　overview　of　the　current　status　of　HMI　research.　We　present　different　sensing　methods,　interactive　control　strategies,　applications,　and　evaluation　methods　and　discuss　the　limitations　and　future　development　directions　in　the　field.　The　results　show　that　the　mainstream　control　methods　of　HMI　are　based　on　motion　signals,　surface　electromyography　(sEMG),　ultrasound　(US),　and　electroencephalogram　(EEG).　In　the　field　of　rehabilitation　robotics,　human　intention　recognition-based　interaction　strategy　is　the　mainstream　HMI　strategy,　which　mainly　collects　biosignals,　force/moment,　spatial　angle,　and　other　information　for　human　intention　recognition.　Future　research　may　focus　on　the　use　of　multimodal　sensing　interactions,　flexible　control　strategies,　and　generalized　rehabilitation　assessment　mechanism.