Zhibin Song

Bio: Zhibin Song is an academic researcher from Kagawa University. The author has contributed to research in topics: Haptic technology & Exoskeleton Device. The author has an hindex of 11, co-authored 26 publications receiving 409 citations. Previous affiliations of Zhibin Song include National Institute of Advanced Industrial Science and Technology.

Development of an upper extremity motor function rehabilitation system and an assessment system

Abstract: This paper presents a novel upper extremity motor function rehabilitation system and an assessment system. The rehabilitation system is an active rehabilitation that can be manipulated by patients through a haptic device and an inertia sensor to perform a tracking task in virtual environment with coordination training of bilateral upper extremity. The design of system aims to augment patients' force exerted by their upper extremity and the ability of force control, namely, dexterity. The structure of rehabilitation system is compact and the inertia of the haptic device's stylus is very small (only 45 g), which makes the system suitable for home-rehabilitation. Simultaneously, in order to assess the effect of rehabilitation, an assessment system has been developed using a 6-axis force sensor. The proposed rehabilitation system is testified experimentally for the upper limbs' rehabilitation training.

Design Process of Exoskeleton Rehabilitation Device and Implementation of Bilateral Upper Limb Motor Movement

Abstract: With the development of neurorehabilitation, physical rehabilitation strategies for the upper limbs have become gradually accepted by therapists and researchers. These strategies include intensive intervention, task-oriented training, and bilateral training. Most upper limb rehabilitation systems have been developed for unilateral training. This paper develops an upper limb exoskeleton rehabilitation device (ULERD) that can be used for bilateral training. The device has three active degrees of freedom (DoFs) in the elbow and wrist joints, and an additional four passive DoFs at these joints to correct any misalignment between the human and device joints. A bilateral training strategy is implemented with the developed ULERD and a haptic device according to neurorehabilitation theory. In a preliminary study, a healthy user was able to manipulate the haptic device with one hand (intact hand for hemiplegic patients) when the upper arm was fixed, and the ULERD assisted in moving the other hand (impaired upper limb for hemiplegic patients). To implement bilateral training, the kinematics of one upper limb (intact limb) and the haptic device is analyzed, respectively. The angles of the three active DoFs are determined via integration. An inertia sensor is used to evaluate the kinematics resolution. The ULERD was evaluated by experienced therapists during the design process to determine its potential for clinic application. Experimental results indicate that the kinematics resolution is effective and that this type of bilateral movement can be implemented using the ULERD and the haptic device.

A Novel Soft Biomimetic Microrobot with Two Motion Attitudes

Abstract: 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.

Implementation of human-machine synchronization control for active rehabilitation using an inertia sensor.

Abstract: 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.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pneumatic Networks for Soft Robotics that Actuate Rapidly

Abstract: Soft robots actuated by infl ation of a pneumatic network (a “pneu-net”) of small channels in elastomeric materials are appealing for producing sophisticated motions with simple controls. Although current designs of pneu-nets achieve motion with large amplitudes, they do so relatively slowly (over seconds). This paper describes a new design for pneu-nets that reduces the amount of gas needed for infl ation of the pneu-net, and thus increases its speed of actuation. A simple actuator can bend from a linear to a quasicircular shape in 50 ms when pressurized at Δ P = 345 kPa. At high rates of pressurization, the path along which the actuator bends depends on this rate. When infl ated fully, the chambers of this new design experience only one-tenth the change in volume of that required for the previous design. This small change in volume requires comparably low levels of strain in the material at maximum amplitudes of actuation, and commensurately low rates of fatigue and failure. This actuator can operate over a million cycles without signifi cant degradation of performance. This design for soft robotic actuators combines high rates of actuation with high reliability of the actuator, and opens new areas of application for them.

Modeling of Soft Fiber-Reinforced Bending Actuators

Abstract: Soft fluidic actuators consisting of elastomeric matrices with embedded flexible materials are of particular interest to the robotics community because they are affordable and can be easily customized to a given application. However, the significant potential of such actuators is currently limited as their design has typically been based on intuition. In this paper, the principle of operation of these actuators is comprehensively analyzed and described through experimentally validated quasi-static analytical and finite-element method models for bending in free space and force generation when in contact with an object. This study provides a set of systematic design rules to help the robotics community create soft actuators by understanding how these vary their outputs as a function of input pressure for a number of geometrical parameters. Additionally, the proposed analytical model is implemented in a controller demonstrating its ability to convert pressure information to bending angle in real time. Such an understanding of soft multimaterial actuators will allow future design concepts to be rapidly iterated and their performance predicted, thus enabling new and innovative applications that produce more complex motions to be explored.

Magnetic assembly of soft robots with hard components

Abstract: Reconfigurable soft robotic actuators with hard components are described. Magnetic attraction is used to couple flexible molded bodies capable of actuation upon pressurization with other flexible molded bodies and/or with hard components (e.g., frames and connectors) to form a seal for fluidic communication and cooperative actuation. Pneumatic de-coupling chambers built into the hard components to de-couple the hard components from the magnetically-coupled soft molded bodies are described. The use of magnetic self-alignment coupling and pneumatic de-coupling allows for the remote assembly and disassembly of complex structures involving hard and soft components. The magnetic coupling allows for rapid, reversible reconfiguration of hybrid soft-hard robots for repair, testing new designs, and carrying out new tasks.