Robot-aided neurorehabilitation: a robot for wrist rehabilitation.

In 1991, a novel robot, MIT-MANUS, was introduced to study the potential that robots might assist in and quantify the neuro-rehabilitation of motor function. MIT-MANUS proved an excellent tool for shoulder and elbow rehabilitation in stroke patients, showing in clinical trials a reduction of impairment in movements confined to the exercised joints. This successful proof of principle as to additional targeted and intensive movement treatment prompted a test of robot training examining other limb segments. This paper focuses on a robot for wrist rehabilitation designed to provide three rotational degrees-of-freedom. The first clinical trial of the device will enroll 200 stroke survivors. Ultimately 160 stroke survivors will train with both the proximal shoulder and elbow MIT-MANUS robot, as well as with the novel distal wrist robot, in addition to 40 stroke survivor controls. So far 52 stroke patients have completed the robot training (ongoing protocol). Here, we report on the initial results on 36 of these volunteers. These results demonstrate that further improvement should be expected by adding additional training to other limb segments.     

A rehabilitation robot with force-position hybrid fuzzy controller: hybrid fuzzy control of rehabilitation robot.

The goal of this study was to design a robot system for assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The robot should be able to guide patient's wrist to move along planned linear or circular trajectories. A hybrid position/force controller incorporating fuzzy logic was developed to constrain the movement in the desired direction and to maintain a constant force along the moving direction. The controller was stable in the application range of movements and forces. Offline analyses of data were used to quantitatively assess the progress of rehabilitation. The results show that the robot could guide the upper limbs of subjects in linear and circular movements under predefined external force levels and apply a desired force along the tangential direction of the movements.     

Patient-cooperative strategies for robot-aided treadmill training: first experimental results.

Task-oriented repetitive movements can improve motor performance in patients with neurological or orthopaedic lesions. The application of robotics and automation technology can serve to assist, enhance, evaluate, and document neurological and orthopedic rehabilitation. This paper deals with the application of "patient-cooperative" techniques to robot-aided gait rehabilitation of neurological disorders. We define patient-cooperative to mean that, during movement, the technical system takes into account the patient's intention and voluntary efforts rather than imposing any predefined movements or inflexible strategies. It is hypothesized that such cooperative robotic approaches can improve the therapeutic outcome compared to classical rehabilitation strategies. New cooperative strategies are presented that detect the patient's voluntary efforts. First, this enables the patient increased freedom of movement by a certain amount of robot compliance. Second, the robot behavior adapts to the existing voluntary motor abilities. And third, the robotic system displays and improves the patient contribution by visual biofeedback. Initial experimental results are presented to evaluate the basic principle and technical function of proposed approaches. Further improvements of the technical design and additional clinical testing is required to prove whether the therapeutic outcome can be enhanced by such cooperative strategies.     

使用集成示波器,让5项常见调试任务更高效

正1前言1前言随着复杂性不断上升,实践证明,现代混合信号设计与设计人员可谓棋逢对手。嵌入式设计工程师必须戴几顶帽子,才能高效地诊断和调试最新设计。这意味着他们需要处理下述活动:设计电源,测量功率效率,在设计中采用无线电,或必须追踪可能威胁预计操作的噪声来源。而且,当今设计要求调试在混合域环境中进行,从DC到RF,包括模拟信号和数字信号、串行总线和并行总线。在不太遥远的过去,这曾要求满满一工作台的仪器,每台仪器都有自己的接口和设置要求。     

Taking a lesson from patients' recovery strategies to optimize training during robot-aided rehabilitation

In robot-assisted neurorehabilitation, matching the task difficulty level to the patient's needs and abilities, both initially and as the relearning process progresses, can enhance the effectiveness of training and improve patients' motivation and outcome. This study presents a Progressive Task Regulation algorithm implemented in a robot for upper limb rehabilitation. It evaluates the patient's performance during training through the computation of robot-measured parameters, and automatically changes the features of the reaching movements, adapting the difficulty level of the motor task to the patient's abilities. In particular, it can select different types of assistance (time-triggered, activity-triggered, and negative assistance) and implement varied therapy practice to promote generalization processes. The algorithm was tuned by assessing the performance data obtained in 22 chronic stroke patients who underwent robotic rehabilitation, in which the difficulty level of the task was manually adjusted by the therapist. Thus, we could verify the patient's recovery strategies and implement task transition rules to match both the patient's and therapist's behavior. In addition, the algorithm was tested in a sample of five chronic stroke patients. The findings show good agreement with the therapist decisions so indicating that it could be useful for the implementation of training protocols allowing individualized and gradual treatment of upper limb disabilities in patients after stroke. The application of this algorithm during robot-assisted therapy should allow an easier management of the different motor tasks administered during training, thereby facilitating the therapist's activity in the treatment of different pathologic conditions of the neuromuscular system.     

Design, implementation and clinical tests of a wire-based robot for neurorehabilitation.

This paper presents the development of and clinical tests on NeReBot (NEuroREhabilitation roBOT): a three degrees-of-freedom (DoF), wire-driven robot for poststroke upper-limb rehabilitation. Basically, the robot consists of a set of three wires independently driven by three electric motors. The wires are connected to the patient's upper limb by means of a splint and are supported by a transportable frame, located above the patient. By controlling wire length, rehabilitation treatment (based on the passive or active-assistive spatial motion of the limb) can be delivered over a wide working space. The arm trajectory is set by the therapist through a very simple teaching-by-showing procedure, enabling most common "hands on" therapy exercises to be reproduced by the robot. Compared to other rehabilitation robots, NeReBot offers the advantages of a low-cost mechanical structure, intrinsically safe treatment thanks to the use of wires, high acceptability by the patient, who does not feel constrained by an "industrial-like" robot, transportability (it can be easily placed aside a hospital bed and/or a wheelchair), and a good trade-off between low number of DoF and spatial performance. These features and the very encouraging results of the first clinical trials make the NeReBot a good candidate for adoption in the rehabilitation treatment of subacute stroke survivors. Clinical trials were performed with a 12-patient experimental group and a 12-patient control group. Resulted that the patients who received robotic therapy in addition to conventional therapy showed greater reductions in motor impairment (in terms of Medical Research Council score, the upper limb subsection of the Fugl-Meyer score, and the Motor Status Score) and improvements in functional abilities (as measured by the Functional Independence Measure and its motor component). No adverse effects occurred and the robotic approach was very well accepted. According to these results, the NeReBot therapy may efficaciously complement standard poststroke multidisciplinary rehabilitation and offer novel therapeutic strategies for neurological rehabilitation.     

Localization and control of a rehabilitation mobile robot by closehuman-machine cooperation

In the field of rehabilitation robotics, a mobile personal robot represents an attractive solution, especially in economic terms in comparison with a desktop workstation. A manipulator arm mounted on a mobile robot can facilitate the restoration of the disabled user's manipulative function. In order both to encourage the person to participate in the task at hand and to be cost effective, close human-machine cooperation is essential. The person controls the robot via a remote station and develops strategies to successfully carry out a mission. The main problems encountered by the person during the execution of a mission are electing to change modes, and the mode transition itself. The authors have examined two aspects of this cooperation: 1) information exchange between human and machine for decision-making and 2) giving to operators complementary and redundant modes to command the system. An experiment has been conducted to study these two aspects. This paper focuses on the control of robot movements in an indoor environment and especially on localization parameters, human-like robot behavior, and the value of proposing complementary control modes to the operator     

Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area.

Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were simulated by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial neural network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p < 0.01) in decoding accuracy as a function of the location of the recording volume. The results suggest that a brain-machine interface (BMI) for dexterous control of individuated fingers and the wrist can be implemented using microelectrode arrays placed broadly in the M1 hand area.     

Robot training of upper limb in multiple sclerosis: comparing protocols with or without manipulative task components

In this pilot study, we compared two protocols for robot-based rehabilitation of upper limb in multiple sclerosis (MS): a protocol involving reaching tasks (RT) requiring arm transport only and a protocol requiring both objects' reaching and manipulation (RMT). Twenty-two MS subjects were assigned to RT or RMT group. Both protocols consisted of eight sessions. During RT training, subjects moved the handle of a planar robotic manipulandum toward circular targets displayed on a screen. RMT protocol required patients to reach and manipulate real objects, by moving the robotic arm equipped with a handle which left the hand free for distal tasks. In both trainings, the robot generated resistive and perturbing forces. Subjects were evaluated with clinical and instrumental tests. The results confirmed that MS patients maintained the ability to adapt to the robot-generated forces and that the rate of motor learning increased across sessions. Robot-therapy significantly reduced arm tremor and improved arm kinematics and functional ability. Compared to RT, RMT protocol induced a significantly larger improvement in movements involving grasp (improvement in Grasp ARAT sub-score: RMT 77.4%, RT 29.5%, p=0.035) but not precision grip. Future studies are needed to evaluate if longer trainings and the use of robotic handles would significantly improve also fine manipulation.     

Selectivity and resolution of surface electrical stimulation for grasp and release

Electrical stimulation of arm and hand muscles can be a functional tool for patients with motor dysfunction. Sufficient stimulation of finger and thumb musculature can support natural grasping function. Yet it remains unclear how different grasping movements can be selectively supported by electrical stimulation. The goal of this study is to determine to what extent activation of individual fingers is possible with surface electrical stimulation for the purpose of rehabilitation following stroke. The extensor digitorum communis (EDC) muscle, flexor pollicis longus (FPL) muscle, and the thenar muscle group, all involved in grasp and release, were selected for stimulation. The evoked forces in individual fingers were measured. Stimulation thresholds and selective ranges were determined for each subject. Electrode locations where the highest selective range occurred were compared between subjects and influences of different isometric wrist positions were assessed. In all subjects selective stimulation of middle finger extension and thumb flexion was possible. In addition, selective stimulation of index and ring finger extension was possible in most cases. In nine out of the ten EDC subjects we were able to stimulate three or all four fingers selectively. However, large variability in electrode locations for high selectivity was observed between the subjects. Within the designs of grasping prostheses and grasping rehabilitation devices, the variability of electrode locations should be taken into account. The results of our study facilitate the optimization of such designs and favour a design which allows individualized stimulation locations.     

Basic experiments on robot-base vibration control of the hot-line work robot system using genetic algorithm

Past studies on hot-line robot systems have focused on the execution of various hot-line works such as the cutout/connection of electric wires and replacement of insulators. Robot-base vibration control methods, however, have seldom been discussed [1–5]. The high-speed operation of the hot-line work robot system, Phase II, in tasks is being worked on with the goal of practical utilization in the near future. Speed increases in boom and manipulator movements are a key part of this. The Phase II system's boom has a natural frequency of 1.0 Hz in commonly used angles. If the boom or the manipulator is rapidly accelerated, the robot base vibrates violently, hampering task execution. This paper proposes a vibration control method, in which control gain and position command acceleration/deceleration time are optimized for the movement of the robot base. In conjunction with the vibration control, we also focus on the optimization for positioning accuracy and preventing overshoot of the manipulator. These three issues are simultaneously solved using the genetic algorithm. The proposed method has been determined for use in a preliminary experiment for the Phase II system development. A simplified test-bed manipulator of the two-axis horizontal scalar type was employed for simulation and experimentation in order to reduce modeling error. The initial effects brought about by the proposed method are verified. © 1998 Scripta Technica, Electr Eng Jpn, 123(2): 40–52, 1998     

Learning, not adaptation, characterizes stroke motor recovery: evidence from kinematic changes induced by robot-assisted therapy in trained and untrained task in the same workspace

Both the American Heart Association and the VA/DoD endorse upper-extremity robot-mediated rehabilitation therapy for stroke care. However, we do not know yet how to optimize therapy for a particular patient's needs. Here, we explore whether we must train patients for each functional task that they must perform during their activities of daily living or alternatively capacitate patients to perform a class of tasks and have therapists assist them later in translating the observed gains into activities of daily living. The former implies that motor adaptation is a better model for motor recovery. The latter implies that motor learning (which allows for generalization) is a better model for motor recovery. We quantified trained and untrained movements performed by 158 recovering stroke patients via 13 metrics, including movement smoothness and submovements. Improvements were observed both in trained and untrained movements suggesting that generalization occurred. Our findings suggest that, as motor recovery progresses, an internal representation of the task is rebuilt by the brain in a process that better resembles motor learning than motor adaptation. Our findings highlight possible improvements for therapeutic algorithms design, suggesting sparse-activity-set training should suffice over exhaustive sets of task specific training.     

无轨道式水轮机叶片坑内修复机器人

介绍了一种多功能的水轮机叶片坑内修复机器人方案和研究进展。机器人本体由可全位置行走的无导轨磁吸附移动平台和多自由度作业机械臂组成,移动平台吸附于待修复的叶片表面,机械臂安装在移动平台上,机械臂可携带检测及各类可更换的作业工具,机器人可以直接在机坑内对叶片受损情况进行自主检测和修复作业（如气刨清理、焊接、打磨等）,操作人员也可通过远端监控系统时机器人的运行状况进行监控,在出现紧急情况时可以直接对机器人进行手动控制。对水轮机叶片坑内修复机器人系统涉及的关键技术进行了研究,仿真及试验结果表明本文提出的水轮机叶片坑内修复机器人方案是可行的,且利用本文研究的关键技术及究成果,可组成水轮机叶片坑内修复机器人系统,为最终实现水轮机叶片坑内修复的自动化提供了技术条件和基础。     

Motion of control of a multijoint robot based on a robust velocity controller

This paper proposes a simple and robust robot motion control method using a robust velocity controller. The robust velocity controller is based on H^∞ control theory, and is called H^∞ velocity controller. The H^∞ velocity controller based motion control method is completely equivalent to the robust acceleration control method using the H^∞ acceleration controller, but it has simpler structure. Therefore, the proposed system can realize a fine robot motion control easily. To confirm the validity of the proposed method, this paper realizes the hybrid control of position and force for a multijoint robot manipulator. Further, the simple realization of hybrid control is proposed considering the attitude of the robot manipulator. This system achieves hybrid control of position and force of the robot manipulator while maintaining a perpendicular attitude to the target environment. The experimental results in this paper show that the proposed system has the desired force and position response to the target environment. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 118 (4): 58–69, 1997     

Virtual reality-enhanced stroke rehabilitation

A personal computer (PC)-based desktop virtual reality (VR) system was developed for rehabilitating hand function in stroke patients. The system uses two input devices, a CyberGlove and a Rutgers Master II-ND (RMII) force feedback glove, allowing user interaction with a virtual environment. This consists of four rehabilitation routines, each designed to exercise one specific parameter of hand movement: range, speed, fractionation or strength. The use of performance-based target levels is designed to increase patient motivation and individualize exercise difficulty to a patient's current state. Pilot clinical trials have been performed using the above system combined with noncomputer tasks, such as pegboard insertion or tracing of 2D patterns. Three chronic stroke patients used this rehabilitation protocol daily for two weeks. Objective measurements showed that each patient showed improvement on most of the hand parameters over the course of the training. Subjective evaluation by the patients was also positive. This technical report focuses on this newly developed technology for VR rehabilitation     

Optimizing Compliant, Model-Based Robotic Assistance to Promote Neurorehabilitation

Based on evidence from recent experiments in motor learning and neurorehabilitation, we hypothesize that three desirable features for a controller for robot-aided movement training following stroke are high mechanical compliance, the ability to assist patients in completing desired movements, and the ability to provide only the minimum assistance necessary. This paper presents a novel controller that successfully exhibits these characteristics. The controller uses a standard model-based, adaptive control approach in order to learn the patient's abilities and assist in completing movements while remaining compliant. Assistance-as-needed is achieved by adding a novel force reducing term to the adaptive control law, which decays the force output from the robot when errors in task execution are small. Several tests are presented using the upper extremity robotic therapy device named Pneu-WREX to evaluate the performance of the adaptive, ldquoassist-as-neededrdquo controller with people who have suffered a stroke. The results of these experiments illustrate the ldquoslackingrdquo behavior of human motor control: given the opportunity, the human patient will reduce his or her output, letting the robotic device do the work for it. The experiments also demonstrate how including the ldquoassist-as-neededrdquo modification in the controller increases participation from the motor system.     

The Development of Two Mobile Gait Rehabilitation Systems

The ability to walk without the help of a caretaker enhances the quality of life for those who are bed-ridden or confined to a wheelchair. At present, most of the available gait rehabilitation robot systems have been designed to support the body weight externally. For gait training to be effective, a mobile body weight support (BWS) mechanism is needed. In mobile gait training robot systems, functions such as patient path following and constant BWS are important issues, particularly in dynamic environments. In the present study, two types of robotic systems were developed for gait rehabilitation. The first is known as the mobile manipulator type and the second the mobile vehicle type. The differences between the two systems in design and control are discussed. A control algorithm based on a neural network was used to compensate for dynamic interactions, unmodeled dynamics, and disturbances by the user on the system. Both electrical and pneumatic BWS mechanisms were built and compared. The proposed BWS systems were tested experimentally for their effectiveness in gait rehabilitation while maximizing the therapeutic outcome.     

Development of a robotic device for facilitating learning by children who have severe disabilities

This paper presents technical aspects of a robot manipulator developed to facilitate learning by young children who are generally unable to grasp objects or speak. The severity of these physical disabilities also limits assessment of their cognitive and language skills and abilities. The CRS robot manipulator was adapted for use by children who have disabilities. Our emphasis is on the technical control aspects of the development of an interface and communication environment between the child and the robot arm. The system is designed so that each child has user control and control procedures that are individually adapted. Control interfaces include large push buttons, keyboards, laser pointer, and head-controlled switches. Preliminary results have shown that young children who have severe disabilities can use the robotic arm system to complete functional play-related tasks. Developed software allows the child to accomplish a series of multistep tasks by activating one or more single switches. Through a single switch press the child can replay a series of preprogrammed movements that have a development sequence. Children using this system engaged in three-step sequential activities and were highly responsive to the robotic tasks. This was in marked contrast to other interventions using toys and computer games.