Zhibin Song

A Novel Soft Biomimetic Microrobot with Two Motion Attitudes

A variety of microrobots have commonly been used in the fields of biomedical engineering and underwater operations during the last few years. Thanks to their compact structure, low driving power, and simple control systems, microrobots can complete a variety of underwater tasks, even in limited spaces. To accomplish our objectives, we previously designed several bio-inspired underwater microrobots with compact structure, flexibility, and multi-functionality, using ionic polymer metal composite (IPMC) actuators. To implement high-position precision for IPMC legs, in the present research, we proposed an electromechanical model of an IPMC actuator and analysed the deformation and actuating force of an equivalent IPMC cantilever beam, which could be used to design biomimetic legs, fingers, or fins for an underwater microrobot. We then evaluated the tip displacement of an IPMC actuator experimentally. The experimental deflections fit the theoretical values very well when the driving frequency was larger than 1 Hz. To realise the necessary multi-functionality for adapting to complex underwater environments, we introduced a walking biomimetic microrobot with two kinds of motion attitudes: a lying state and a standing state. The microrobot uses eleven IPMC actuators to move and two shape memory alloy (SMA) actuators to change its motion attitude. In the lying state, the microrobot implements stick-insect-inspired walking/rotating motion, fish-like swimming motion, horizontal grasping motion, and floating motion. In the standing state, it implements inchworm-inspired crawling motion in two horizontal directions and grasping motion in the vertical direction. We constructed a prototype of this biomimetic microrobot and evaluated its walking, rotating, and floating speeds experimentally. The experimental results indicated that the robot could attain a maximum walking speed of 3.6 mm/s, a maximum rotational speed of 9°/s, and a maximum floating speed of 7.14 mm/s. Obstacle-avoidance and swimming experiments were also carried out to demonstrate its multi-functionality.

A novel motor function training assisted system for upper limbs rehabilitation

In this paper, we propose a novel task-oriented motor function training and assistance of upper limbs system after brain injured such as stroke based on Virtual-Reality. In this system, two kinds of training approaches are developed. One is tracking training with path-unlimited based on a mass-spring-damper force model, and the other is tracking training with path-limited based on a compound force model. Both of training approaches are same that coordination motion of two hands is needed. We want to re-examine how effective the haptic sensory and visual sensory are in training of upper limbs. Further, we enhance the effect of system through adding assistance in order to help mild stroke patients to recovery. This system is convenient and compact so that it is suitable for home-based rehabilitation.

Implementation of self-rehabilitation for upper limb based on a haptic device and an exoskeleton device

Rehabilitation based on robot has been important researches field. In this paper, an exoskeleton device for upper limb has been developed, and it includes three active DoFs (Degree of Freedoms) and four passive DoFs. This device is used to assist performance for the impaired upper limb. Hemiplegic patients can move their intact upper limb by manipulating a haptic device (Phantom Premium) and their impaired upper limb can move synchronously, which is driven by the exoskeleton device. Different from general joystick, haptic device not only exert force to patients, but also can detect the movement of upper limb because of its 6 DoFs and enough work range. Therefore, the impaired upper limb can perform following the intact upper limb and patients can perform some rehabilitation by themselves. In this paper we focused on the motion detection of intact limb. Control on exoskeleton device was discussed in previous work. Also this system can be used in remote rehabilitation.

VR-based a novel active rehabilitation system for upper limbs

In this paper, VR-based a novel rehabilitation system has been developed to help mild stroke patients to restore their motor function of upper limbs. The system consists of haptic device (PHANTOM Omni), an advanced inertial sensor (MTx) and a computer. Subject engages in task-oriented interactions with object with his eyes looking at computers screen. We suppose one of the subjects hands is intact, the other one is injured. Subject wears the glove installed inertial sensor on intact hand, and manipulates the stylus of haptic device with injured hand. A feasible force model has been created and force can be exerted on the stylus. Subject need coordinate his two hands to accomplish the task. The remotion of injured hand can be assisted through rotating the inertial sensor around some direction when assistance is needed. Subject is expected to facilitate cortical plasticity and to improve the agility and strength of manipulators hands by engaging cortical motor observation, planning and execution.

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.

Implementation of Human-Machine Synchronization Control for Active Rehabilitation Using an Inertia Sensor

According to neuro-rehabilitation practice, active training is effective for mild stroke patients, which means these patients are able to recovery effective when they perform the training to overcome certain resistance by themselves. Therefore, for rehabilitation devices without backdrivability, implementation of human-machine synchronization is important and a precondition to perform active training. In this paper, a method to implement this precondition is proposed and applied in a user’s performance of elbow flexions and extensions when he wore an upper limb exoskeleton rehabilitation device (ULERD), which is portable, wearable and non-backdrivable. In this method, an inertia sensor is adapted to detect the motion of the user’s forearm. In order to get a smooth value of the velocity of the user’s forearm, an adaptive weighted average filtering is applied. On the other hand, to obtain accurate tracking performance, a double close-loop control is proposed to realize real-time and stable tracking. Experiments have been conducted to prove that these methods are effective and feasible for active rehabilitation.

Development of a potential system for upper limb rehabilitation training based on virtual reality

This paper proposed a novel rehabilitation system for rehabilitation training of the upper limbs for patients whose brain injured such as stroke. We also proposed some strategies of rehabilitation using this system based on Virtual Reality. In this paper, Virtual Reality (VR) was adapted in this system in which the water simulation provided fundamental environment for rehabilitation training and we used a haptic device (Phantom Omni) and an inertial sensor (MTx) to implement tasks proposed. In this paper, three types of tasks were designed so that comprehensive analyses of performance would be obtained. As preliminary phase, five healthy subjects were invited to participate in experiments. The experimental results showed that the virtual force model was effective for the upper limbs rehabilitation and the subjects showed improvement during the experiment. Though there is some limitation due to the haptic device, this system is promising, because strategies proposed are potential to be used in real rehabilitation.

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.