## 1 Introduction  
Rehabilitation robots have received considerable attention in recent years due to the increasing tendency toward rehabilitation and the shortage of rehabilitation physicians. According to statistics, one out of every six people in the world may suffer from stroke [1], making it a global problem that seriously endangers human health. In China, approximately 2.5 million new brain injuries and spinal cord injuries occur each year [2]. These patients need rehabilitation treatment after surgery to restore normal function, but few rehabilitation medical resources exist. There is an urgent need for approximately 350,000 rehabilitation technicians, but fewer than 20,000 professionals currently work on rehabilitation. In addition, traditional physical therapy is labor intensive and repetitive [3]; therefore, rehabilitation robots have emerged. A rehabilitation robot is a device that assists rehabilitation therapists in repetitive rehabilitation training tasks based on neurological rehabilitation mechanisms. In addition to undertaking intensive,repetitive rehabilitation training, modern rehabilitation robots should also have the ability to recognize the patient's motor intent and physical condition. If given such recognition abilities, a high-level rehabilitation strategy could be achieved. In recent years, robot-aided training strategies that support functional, assist-as-needed, or challenging training regimens have been widely explored. These strategies can better provide motor plasticity. The primary goal of alternative robot-aided training strategies is to accurately recognize the various patient states in real time to provide an auxiliary judgment basis for the top-level control system and to provide the necessary control input for the bottom controller. These states include the patient's motor intent and tolerance degree, the physical interaction (i.e., asynchronous error) between the patient and the robot, the patient's abnormal motor pattern (e.g., abnormal gait) and the tasks that must be completed for rehabilitation assessment [4], and patient fall detection. Among these, fall detection is critical, because there is a high probability of falls during rehabilitation, and these falls can cause severe injuries. Statistically, the probability of a stroke survivor falling within six months after surgery is 25%-37%, and subsequently, the fall probability is 23%-50% [5; 6; 7; 8]. Accidental falls and fall-related injuries, such as hip fractures, often lead to severe disabilities and affect the patient's overall health [9]. Therefore, timely and effective fall prediction and protection during rehabilitation training are beneficial for stroke patients. The detection of abnormal gait (patient-specific gait) [10] and the fall methods currently used in rehabilitation training can be roughly divided into three methods: image sensing, environmental sensors, and wearable sensors. Image detection works mainly through cameras installed in the environment, and uses image-processing algorithms to detect whether a patient has fallen [11]. This approach does not require patients to wear testing equipment; however, the image-processing algorithms are sophisticated and easily affected by light and noise, which degrades the accuracy and applies only to fixed locations, providing little applicability to a wider range of activities. Infrared sensors and acoustic sensors are generally installed [12; 13] to detect whether a fall occurs in a specific environment. This approach is inexpensive, but it is limited to environments where the sensors are installed. Wearable sensor methods [14; 15; 16; 17; 18; 19] mainly analyze the acceleration sensor data of limbs and then use an algorithm to identify falls. Wearable sensors have been widely studied and applied because of their small volume and low cost.