The surface electromyography (sEMG) technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The purpose of this paper is to compare eight combinations of four feature extraction methods (Root Mean Square (RMS), Detrended Fluctuation Analysis (DFA), Weight Peaks (WP), and Muscular Model (MM)) and two classifiers (Neural Networks (NN) and Support Vector Machine (SVM)), for the task of mapping sEMG signals to eight upper-limb motions, to find out the relation between these methods and propose a proper combination to solve this issue. Seven subjects participated in the experiment and six muscles of the upper-limb were selected to record sEMG signals. The experimental results showed that NN classifier obtained the highest recognition accuracy rate (88.7%) during the training process while SVM performed better in real-time experiments (85.9%). For time consumption, SVM took less time than NN during the training process but needed more time for real-time computation. Among the four feature extraction methods, WP had the highest recognition rate for the training process (97.7%) while MM performed the best during real-time tests (94.3%). The combination of MM and NN is recommended for strict real-time applications while a combination of MM and SVM will be more suitable when time consumption is not a key requirement.

Many stroke patients are expected to rehabilitate at home, which limits their access to proper rehabilitation equipment, treatment, or assessment by therapists. We have developed a novel telerehabilitation system that incorporates a human-upper-limb-like device and an exoskeleton device. The system is designed to provide the feeling of real therapist–patient contact via telerehabilitation. We applied the principle of a series elastic actuator to both the master and slave devices. On the master side, the therapist can operate the device in a rehabilitation center. When performing passive training, the master device can detect the therapist’s motion while controlling the deflection of elastic elements to near-zero, and the patient can receive the motion via the exoskeleton device. When performing active training, the design of the force-sensing mechanism in the master device can detect the assisting force added by the therapist. The force-sensing mechanism also allows force detection with an angle sensor. Patients’ safety is guaranteed by monitoring the motor’s current from the exoskeleton device. To compensate for any possible time delay or data loss, a torque-limiter mechanism was also designed in the exoskeleton device for patients’ safety. Finally, we successfully performed a system performance test for passive training with transmission control protocol/internet protocol communication.

As an important branch of medical robotics, a rehabilitation training robot for the hemiplegic upper limbs is a research hotspot of rehabilitation training. Based on the motion relearning program, rehabilitation technology, human anatomy, mechanics, computer science, robotics, and other fields of technology are covered. Based on an sEMG real-time training system for rehabilitation, the exoskeleton robot still has some problems that need to be solved in this field. Most of the existing rehabilitation exoskeleton robotic systems are heavy, and it is difficult to ensure the accuracy and real-time performance of sEMG signals. In this paper, we design a real-time training system for the upper limb exoskeleton robot based on the EMG signal. It has four main characteristics: light weight, portability, high precision, and low delay. This work includes the structure of the rehabilitation robotic system and the method of signal processing of the sEMG. An experiment on the accuracy and time delay of the sEMG signal processing has been done. In the experimental results, the recognition accuracy of the sEMG is 94%, and the average delay time is 300 ms, which meets the accuracy and real-time requirements.

This study aims to observe and evaluate the use of remifentanil in conscious sedation and analgesia for the safety and comfort of patients undergoing short-term endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic sphincterotomy (EST). Sixty-eight patients who underwent ERCP and EST were randomly divided into two groups: research group and control group. Patients in the research group were intravenously injected with remifentanil (80–2/3* age) for 1 to 2 minutes, combined with the intravenous injection of propofol (20–30 mg) during the course of treatment. ERCP surgery was performed when Ramsay sedation scale (RSS) score reached 2–3. During the surgery, patients were closely monitored for cough symptoms, aspiration, and respiratory and circulatory system performance, and timely treatment was performed. Sedative drugs were not given in patients in the control group. In research group, the circulatory and respiratory depression of patients was mild, only one patient needed to be treated, and there was no arrhythmia requiring treatment. Five patients had respiratory depression (blood oxygen saturation decreased to <90%), which was immediately corrected. There were no interruptions during surgery due to body movement, cough, or aspiration. The use of remifentanil for conscious sedation and analgesia can be broadly applied in short-term ERCP, which greatly improves patient comfort during the surgery. This approach may bear promise for a widespread use in future clinical practice.

As we know that MIS permits vascular interventions through very small incisions and minimizes the patients' trauma and permits a faster recovery compared. In this document, we present the mechanical and haptic simulation of the MIS VR operation training system. Virtual reality technology for doctors can improve the accuracy and safety of real vascular interventional surgery for vascular interventional surgery in local or remote training. It consists of a master controller system at surgery side and the catheter manipulator placed at the patient side. For the slave side Virtual Reality based Robotic Catheter System, we want to realize the 3D image and catheter control of the Virtual Reality System allows generating realistic geometrical model of catheter and model of blood vessels, and force feeling of surgeons. Finally, we complete the analysis and simulation of the model haptic deformation, develop the catheter control and mechanical design of the Virtual Reality based Robotic Catheter System and the experimental results show the mechanical and haptic analysis of the VR training system.

Robot technology made rapid development in the medical surgical and remote communication technology promote the development of telemedicine. Vascular interventional robot has become a promising technology. To assess the clinical usefulness, accuracy, and safety of remote angiography, we used vascular interventional robot system. Vascular interventional robot system is designed to complete the remote cerebral angiography surgery. Experts in Japan had controlled the slave part of robot in Beijing through operating the host part of robot, and performed cerebral angiography in dog. The positioning accuracy, usefulness and safety were observed. Animal experiment was successful with no complications. The whole procedure included incubating and angiography of common carotid artery and vertebral artery in both sides, it took 45 minutes. The remote positioning accuracy was 1 mm. The result indicates that remote angiography using vascular interventional robot is reliable and safe. In the future, we think that the remote manipulation will advance collaboration between surgeons, develop sharing of resources, and have good prospect in the field of vascular intervention surgery.