18 research outputs found
Robot Assisted Shoulder Rehabilitation: Biomechanical Modelling, Design and Performance Evaluation
The upper limb rehabilitation robots have made it possible to improve the motor recovery in stroke survivors while reducing the burden on physical therapists. Compared to manual arm training, robot-supported training can be more intensive, of longer duration, repetitive and task-oriented. To be aligned with the most biomechanically complex joint of human body, the shoulder, specific considerations have to be made in the design of robotic shoulder exoskeletons. It is important to assist all shoulder degrees-of-freedom (DOFs) when implementing robotic exoskeletons for rehabilitation purposes to increase the range of motion (ROM) and avoid any joint axes misalignments between the robot and human’s shoulder that cause undesirable interaction forces and discomfort to the user.
The main objective of this work is to design a safe and a robotic exoskeleton for shoulder rehabilitation with physiologically correct movements, lightweight modules, self-alignment characteristics and large workspace. To achieve this goal a comprehensive review of the existing shoulder rehabilitation exoskeletons is conducted first to outline their main advantages and disadvantages, drawbacks and limitations. The research has then focused on biomechanics of the human shoulder which is studied in detail using robotic analysis techniques, i.e. the human shoulder is modelled as a mechanism. The coupled constrained structure of the robotic exoskeleton connected to a human shoulder is considered as a hybrid human-robot mechanism to solve the problem of joint axes misalignments. Finally, a real-scale prototype of the robotic shoulder rehabilitation exoskeleton was built to test its operation and its ability for shoulder rehabilitation
Robotics 2010
Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development
Climbing and Walking Robots
With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information
Biomechatronics: Harmonizing Mechatronic Systems with Human Beings
This eBook provides a comprehensive treatise on modern biomechatronic systems
centred around human applications. A particular emphasis is given to exoskeleton
designs for assistance and training with advanced interfaces in human-machine
interaction. Some of these designs are validated with experimental results which
the reader will find very informative as building-blocks for designing such systems.
This eBook will be ideally suited to those researching in biomechatronic area with
bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design
at post-graduate level
System Identification of Bipedal Locomotion in Robots and Humans
The ability to perform a healthy walking gait can be altered in numerous cases due to gait disorder related pathologies. The latter could lead to partial or complete mobility loss, which affects the patients’ quality of life. Wearable exoskeletons and active prosthetics have been considered as a key component to remedy this mobility loss. The control of such devices knows numerous challenges that are yet to be addressed. As opposed to fixed trajectories control, real-time adaptive reference generation control is likely to provide the wearer with more intent control over the powered device. We propose a novel gait pattern generator for the control of such devices, taking advantage of the inter-joint coordination in the human gait. Our proposed method puts the user in the control loop as it maps the motion of healthy limbs to that of the affected one. To design such control strategy, it is critical to understand the dynamics behind bipedal walking. We begin by studying the simple compass gait walker. We examine the well-known Virtual Constraints method of controlling bipedal robots in the image of the compass gait. In addition, we provide both the mechanical and control design of an affordable research platform for bipedal dynamic walking. We then extend the concept of virtual constraints to human locomotion, where we investigate the accuracy of predicting lower limb joints angular position and velocity from the motion of the other limbs. Data from nine healthy subjects performing specific locomotion tasks were collected and are made available online. A successful prediction of the hip, knee, and ankle joints was achieved in different scenarios. It was also found that the motion of the cane alone has sufficient information to help predict good trajectories for the lower limb in stairs ascent. Better estimates were obtained using additional information from arm joints. We also explored the prediction of knee and ankle trajectories from the motion of the hip joints
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A hand exoskeleton with series elastic actuation for rehabilitation : design, control and experimentation
Rehabilitation of the hands is critical for restoring independence in activities of daily living for individuals with upper extremity disabilities. Conventional therapies for hand rehabilitation have not shown significant improvement in hand function. Robotic exoskeletons have been developed to assist in therapy and there is initial evidence that such devices with force-control based strategies can help in effective rehabilitation of human limbs. However, to the best of our knowledge, none of the existing hand exoskeletons allow for accurate force or torque control. In this dissertation, we design and prototype a novel hand exoskeleton that has the following unique features: (i) Bowden-cable-based series elastic actuation allowing for bidirectional torque control of each joint individually, (ii) an underlying kinematic mechanism that is optimized to achieve large range of motion and (iii) a thumb module that allows for independent actuation of the four thumb joints. To control the developed hand exoskeleton for efficacious rehabilitation after a neuromuscular impairment such as stroke, we present two types of subject-specific assist-as-needed controllers. Learned force-field control is a novel control technique in which a neural-network-based model of the required torques given the joint angles for a specific subject is learned and then used to build a force-field to assist the joint motion of the subject to follow a trajectory designed in the joint-angle space. Adaptive assist-as-needed control, on the other hand, estimates the coupled digit-exoskeleton system torque requirement of a subject using radial basis function (RBF) and on-the-y adapts the RBF magnitudes to provide a feed-forward assistance for improved trajectory tracking. Experiments with healthy human subjects showed that each controller has its own trade-offs and is suitable for a specific type of impairment. Finally, to promote and optimize motor (re)-learning, we present a framework for robot-assisted motor (re)-learning that provides subject-specific training by allowing for simultaneous adaptation of task, assistance and feedback based on the performance of the subject on the task. To train the subjects for dexterous manipulation, we present a torque-based task that requires subjects to dynamically regulate their joint torques. A pilot study carried out with healthy human subjects using the developed hand exoskeleton suggests that training under simultaneous adaptation of task, assistance and feedback can module challenge and affect their motor learning.Mechanical Engineerin
Model-based Control of Upper Extremity Human-Robot Rehabilitation Systems
Stroke rehabilitation technologies have focused on reducing treatment cost while improving effectiveness. Rehabilitation robots are generally developed for home and clinical usage to: 1) deliver repetitive and stimulating practice to post-stroke patients, 2) minimize therapist interventions, and 3) increase the number of patients per therapist, thereby decreasing the associated cost. The control of rehabilitation robots is often limited to black- or gray-box approaches; thus, safety issues regarding the human-robot interaction are not easily considered. Furthermore, despite numerous studies of control strategies for rehabilitation, there are very few rehabilitation robots in which the tasks are implemented using optimal control theory. Optimal controllers using physics-based models have the potential to overcome these issues. This thesis presents advanced impedance- and model-based controllers for an end-effector-based upper extremity stroke rehabilitation robot. The final goal is to implement a biomechanically-plausible real-time nonlinear model predictive control for the studied rehabilitation system. The real-time term indicates that the controller computations finish within the sampling frequency time. This control structure, along with advanced impedance-based controllers, can be applied to any human-environment interactions. This makes them promising tools for different types of assistive devices, exoskeletons, active prostheses and orthoses, and exercise equipment. In this thesis, a high-fidelity biomechatronic model of the human-robot interaction is developed. The rehabilitation robot is a 2 degree-of-freedom parallelogram linkage with joint friction and backlash, and nonlinear dynamics. The mechatronic model of the robot with relatively accurate identified dynamic parameters is used in the human-robot interaction plant. Different musculoskeletal upper extremity, biomechanic, models are used to model human body motions while interacting with the rehabilitation robot model. Human-robot interaction models are recruited for model-in-loop simulations, thereby tuning the developed controllers in a structured resolution. The interaction models are optimized for real-time simulations. Thus, they are also used within the model-based control structures to provide biofeedback during a rehabilitation therapy. In robotic rehabilitation, because of physical interaction of the patient with a mechanical device, safety is a fundamental element in the design of a controller. Thus, impedance-based assistance is commonly used for robotic rehabilitation. One of our objectives is to achieve a reliable and real-time implementable controller. In our definition, a reliable controller is capable of handling variable exercises and admittance interactions. The controller should reduce therapist intervention and improve the quality of the rehabilitation. Hence, we develop advanced impedance-based assistance controllers for the rehabilitation robot. Overall, two types of impedance-based (i.e., hybrid force-impedance and optimal impedance) controllers are developed and tuned using model-in-loop simulations. Their performances are assessed using simulations and/or experiments. Furthermore, their drawbacks are discussed and possible methods for their improvements are proposed. In contrast to black/gray-box controllers, a physics-based model can leverage the inherent dynamics of the system and facilitate implementation of special control techniques, which can optimize a specific performance criterion while meeting stringent system constraints. Thus, we present model-based controllers for the upper extremity rehabilitation robot using our developed musculoskeletal models. Two types of model-based controllers (i.e., nonlinear model predictive control using external 3-dimensional musculoskeletal model or internal 2-dimensional musculoskeletal model) are proposed. Their performances are evaluated in simulations and/or experiments. The biomechanically-plausible nonlinear model predictive control using internal 2-dimensional musculoskeletal model predicts muscular activities of the human subject and provides optimal assistance in real-time experiments, thereby conforming to our final goal for this project
回復期脳卒中患者におけるロボットスーツHAL®福祉用を用いた歩行練習の効果および健康関連QOLに関する研究
筑波大学 (University of Tsukuba)201
Development of a hybrid robotic system based on an adaptive and associative assistance for rehabilitation of reaching movement after stroke
Stroke causes irreversible neurological damage. Depending on the location and the size of
this brain injury, different body functions could result affected. One of the most common
consequences is motor impairments. The level of motor impairment affectation varies between
post-stroke subjects, but often, it hampers the execution of most activities of daily living.
Consequently, the quality of life of the stroke population is severely decreased.
The rehabilitation of the upper-limb motor functions has gained special attention in the
scientific community due the poor reported prognosis of post-stroke patients for recovering
normal upper-extremity function after standard rehabilitation therapy. Driven by the advance
of technology and the design of new rehabilitation methods, the use of robot devices,
functional electrical stimulation and brain-computer interfaces as a neuromodulation system
is proposed as a novel and promising rehabilitation tools. Although the uses of these technologies
present potential benefits with respect to standard rehabilitation methods, there still
are some milestones to be addressed for the consolidation of these methods and techniques
in clinical settings.
Mentioned evidences reflect the motivation for this dissertation. This thesis presents the
development and validation of a hybrid robotic system based on an adaptive and associative
assistance for rehabilitation of reaching movements in post-stroke subjects. The hybrid
concept refers the combined use of robotic devices with functional electrical stimulation.
Adaptive feature states a tailored assistance according to the users’ motor residual capabilities,
while the associative term denotes a precise pairing between the users’ motor intent
and the peripheral hybrid assistance. The development of the hybrid platform comprised the
following tasks:
1. The identification of the current challenges for hybrid robotic system, considering twofold
perspectives: technological and clinical. The hybrid systems submitted in literature
were critically reviewed for such purpose. These identified features will lead the
subsequent development and method framed in this work.
2. The development and validation of a hybrid robotic system, combining a mechanical
exoskeleton with functional electrical stimulation to assist the execution of functional
reaching movements. Several subsystems are integrated within the hybrid platform,
which interact each other to cooperatively complement the rehabilitation task. Complementary,
the implementation of a controller based on functional electrical stimulation
to dynamically adjust the level of assistance is addressed. The controller is conceived to
tackle one of the main limitations when using electrical stimulation, i.e. the highly nonlinear
and time-varying muscle response. An experimental procedure was conducted
with healthy and post-stroke patients to corroborate the technical feasibility and the
usability evaluation of the system.
3. The implementation of an associative strategy within the hybrid platform. Three different
strategies based on electroencephalography and electromyography signals were
analytically compared. The main idea is to provide a precise temporal association between
the hybrid assistance delivered at the periphery (arm muscles) and the users’
own intention to move and to configure a feasible clinical setup to be use in real rehabilitation
scenarios.
4. Carry out a comprehensive pilot clinical intervention considering a small cohort of
patient with post-stroke patients to evaluate the different proposed concepts and assess
the feasibility of using the hybrid system in rehabilitation settings.
In summary, the works here presented prove the feasibility of using the hybrid robotic system
as a rehabilitative tool with post-stroke subjects. Moreover, it is demonstrated the adaptive
controller is able to adjust the level of assistance to achieve successful tracking movement
with the affected arm. Remarkably, the accurate association in time between motor cortex
activation, represented through the motor-related cortical potential measured with electroencephalography,
and the supplied hybrid assistance during the execution of functional (multidegree
of freedom) reaching movement facilitate distributed cortical plasticity. These results
encourage the validation of the overall hybrid concept in a large clinical trial including an
increased number of patients with a control group, in order to achieve more robust clinical
results and confirm the presented herein.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Ramón Ceres Ruiz.- Secretario: Luis Enrique Moreno Lorente.- Vocal: Antonio Olivier
Effects of gait training using the Hybrid Assistive Limb® in recovery-phase stroke patients: A 2-month follow-up, randomized, controlled study
BACKGROUND: Gait training using the Hybrid Assistive Limb® (HAL®) may have beneficial effects on post-stroke gait function and independent walking. However, the long-term and medium-term efficacies of gait training using HAL® in stroke patients remain unclear. OBJECTIVE: To compare the medium-term efficacy of gait training using a single-leg version of the Hybrid Assistive Limb® (HAL®) on the paretic side with conventional gait training (CGT) in recovery-phase stroke patients. METHODS: Twenty-four post-stroke participants (HAL® group: n = 12, CGT group: n = 12) completed the trial. Over 4 weeks, all participants received twelve 20-min sessions of either HAL® (using the single-leg version of HAL® on the paretic side) or conventional (performed by skilled and experienced physical therapists) gait training. Outcome measures were evaluated prior to training, after 12 sessions, and at 8 and 12 weeks after intervention initiation. Functional Ambulation Category (FAC) was the primary outcome measure. RESULTS: The HAL® group showed significant improvement in FAC after 12 sessions, and at 8 and 12 weeks compared to the conventional group (P = 0.02). CONCLUSIONS: The results suggested that a gait training program based on HAL® may improve independent walking more efficiently than CGT at 1 and 2 months after intervention