Songyuan Zhang

Design of a Novel Telerehabilitation System with a Force-Sensing Mechanism

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.

A surface EMG signals-based real-time continuous recognition for the upper limb multi-motion

This paper was aimed at the continuous recognition of the upper limb multi-motion during the upper limb movement for rehabilitation training. The amplitude of the surface electromyographic (sEMG) signals change during movement of the upper limb and the features of sEMG signals are different with the changes. These variances in the features represent the different statuses of the upper limb. Recognizing the variances will lead to recognition of the upper limb motion. In this study, sEMG signals were recorded through five noninvasive electrodes attached on the anatomy points of the upper limb and an autoregressive model was used to extract the features of the detected sEMG signals. After that the Back-propagation Neural Networks was applied to recognize the patterns of the upper arm motion using the variant features as the training and input data. Three volunteers participated in the real-time experiment and the results stated that this method is effective for a real-time continuous recognition of the upper limb multi-motions.

Characteristic Evaluation of a Shrouded Propeller Mechanism for a Magnetic Actuated Microrobot

Medical microrobots have been widely used in clinical applications, particularly the spiral type locomotion mechanism, which was recently considered one of the main self-propelling mechanisms for the next medical microrobot to perform tasks such as capsule endoscopy and drug delivery. However, limits in clinical applications still exist. The spiral action of the microrobot while being used for diagnosis may lead to pain or even damage to the intestinal wall due to the exposed mechanisms. Therefore, a new locomotive mechanism, named the shrouded propeller mechanism, was proposed to achieve a high level of medical safety as well as effective propulsive performance in our study. The shrouded propeller mechanism consists of a bare spiral propeller and a non-rotating nozzle. To obtain a high effective propulsive performance, two types of screw grooves with different shapes including the cylindrical screw groove and the rectangular screw groove with different parameters were analyzed using the shrouded model. Two types of magnetic actuated microrobots with different driving modes, the electromagnetic (three-pole rotor) actuated microrobot and the permanent magnet (O-ring type magnet) actuated microrobot were designed to evaluate the performance of the electromagnetic actuation system. Based on experimental results, the propulsive force of the proposed magnetic actuated microrobot with a shrouded propeller was larger than the magnetic actuated microrobot with a bare spiral propeller under the same parameters. Additionally, the shrouded propeller mechanism as an actuator can be used for other medical microrobots for flexible locomotion.

Recognition of motion of human upper limb using sEMG in real time: Towards bilateral rehabilitation

The surface electromyographic (sEMG) signal has been researched in many fields, such as medical diagnoses and prostheses control. In this paper, recognition of motion of human upper limb by processing sEMG signal in real time was proposed for application in bilateral rehabilitation, in which hemiplegia patients trained their impaired limbs by rehabilitation device based on motion of the intact limbs. In the processing of feature exaction of sEMG, Wavelet packet transform (WPT) and autoregressive (AR) model were used. The effect of feature exaction with both methods was discussed through the processing of classification where Back-propagation Neural Networks were trained. The experimental results show both methods can obtain reliable accuracy of motion pattern recognition. Moreover, on the experimental condition, the recognized accuracy of WPT is higher than that of AR model.

Design of a master-slave rehabilitation system using self-tuning fuzzy PI controller

Many robotic devices have been developed for stroke patients to recover their upper limb motor function. Among them, master-slave type rehabilitation systems provide surveillance of the therapist to the patient who is performing home-rehabilitation. In this study, we proposed a wearable and light exoskeleton device for upper limb rehabilitation and designed a master-slave rehabilitation system using the exoskeleton device as slave device and a haptic device (Phantom Premium) as master device. To convey therapists experience to patients using this system, the slave device is driven to track the motion of the master device manipulated by the therapist. In order to improve the tracking efficacy of traditional PI control, a self-tuning fuzzy PI control was proposed. Results of simulation indicated the proposed control method is more effective than the traditional PI control, particularly in tracking accuracy and response speed.

Study on recognition of upper limb motion pattern using surface EMG signals for bilateral rehabilitation

Surface electromyographic signal (sEMG) is deep related with the activation of motor muscle and motion of human body, which can be used to estimate the intention of the human movement. So it is advantaged in the application of bilateral rehabilitation, where hemiplegic patients can perform rehabilitation training to their impaired limbs following the motion of intact limbs by using a certain training tool. In this paper, we discussed the motion pattern recognition of human upper limb based on the sEMG signals. The main features of motion patterns based on sEMG signals are extracted via wavelet packet transform. Because the sEMG signal is a kind of non-stationary signal and there are many factors which can affect it like inherent noise, cross talk and so on. Therefore, a simple new method to obtain the trend of sEMG with weighted peaks as features was proposed and support vector machine (SVM) is utilized as the classifier. The contrastive experimental results show that the proposed method improved the recognition rate.

ULERD-based active training for upper limb rehabilitation

In this paper, we proposed a control method to implement the upper-limb active training which is performed with the proposed exoskeleton device. It provides a wide approach for Human Machine Interface (HMI) in which the device is of high inertia, high friction and non-backdrivability and it is difficult to obtain the contact force between human and the device directly. The main idea of this method is to measure the motion of human body rather than the motion of device. This method is more suitable to the HMI in which the contact between human and device can be assumed as a spring-damper model. According to two kinds of experiments designed, different contact resistance was exerted to the forearm of the user. The sEMG signals detected from biceps brachii and triceps brachii were processed and the two kinds of resistance exerted to human forearm were confirmed.

Performance evaluation of a magnetic microrobot driven by rotational magnetic field

Wireless capsule microrobots have potential to radically accomplish many medical procedures. Various electromagnetic actuation control systems are utilized to realize the motion control of the wireless capsule microrobot. In this paper, a tele-operation system provides telepresence by allowing a doctor to remotely control a wireless capsule microrobot through a master device. Based on a simplified mechanical model, we also analyzed and developed a wireless microrobot. And then we obtained the relationship between the magnetic flux density changing frequency and the moving speed to realize the real time control and flexible motion by experiments. The experimental results appear in a good performance on flexibility.

Development of a Bilateral Rehabilitation Training System Using the Haptic Device and Inertia Sensors

According to the neuro-rehabilitation theory, passive, resistance and bilateral training are commonly applied for recovering the motor-function of stroke patient. Among them, bilateral training is proved to be an effective method for the hemiparesis that occupies most part of stroke patients. In this article, a novel system is proposed for providing the bilateral training with coordinative motion of two limbs. This system is developed for the elbow function recovery and the motion of two limbs is detected with two inertia sensors. A commercial haptic device (Phantom Premium) is adopted for providing a feedback with information of errors and how to correct them. Combined with a graphic interface which provides a visual feedback, the patient can adjust the two limbs to a coordinative motion. This system can perform the training to those patients with some muscle strength. However, usually the rehabilitation training is hierarchical and those patients with little muscle strength can even not lift their own limbs. Therefore, a light-weight exoskeleton device is applied and this device could provide partial assisting force, thus the patient can gradually adapt to the training. In this article, an issue about the effectiveness of feedback is discussed and verified with several contrast experiments.

Finger joint continuous interpretation based on sEMG signals and muscular model

The human hand is very dexterous and can perform various of gestures in activities of daily living. Only dividing the motions of hand into several types and applying pattern recognition method for implementation of manipulation control may result in low dexterity and delicacy. In this paper, a novel finger joint interpretation method based on sEMG signals and muscular model is presented. The motion of finger is flexion and extension without any external resistant force and at a natural movement velocity. sEMG signals are recorded from flexor digitorum superficialis and extensor digitorum of the forearm. The Hill-based muscular model is used to calculate the force of muscles according to sEMG signals. In this paper, we assume that the changing of force corresponds directly to the motions of fingers given the circumstance that the subjects hold nothing in their hand and keep the movement velocity. The curve fitting method and Kalman filter are implemented to calculate the relation between force and basic movements of digits. Five subjects participated in the experiment to evaluate the efficiency of this method.