The Human–Machine Interaction Methods and Strategies for Upper and Lower Extremity Rehabilitation Robots: A Review

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.