3,679 research outputs found

    Design of an exoskeleton for upper limb robot-assisted rehabilitation based on co-simulation

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    This paper presents the design and the simulation of an exoskeleton based on the kinematics of the human arm intended to be used in robot-assisted rehabilitation of the upper limb. The design meets the kinematic characteristics of the human arm so that the exoskeleton allows the movement of the arm in its full range of motion. We used co-simulation to design the exoskeleton considering a model of the upper limb developed in Opensim, Solidworks to design the mechanical structure and Matlab to construct the dynamic model. The system in motion was simulated in Simmechanics using predictive dynamics to compute independent joint trajectories obtained by modelling the exoskeleton as several optimization problems solved with SNOPT from Tomlab. The use of virtual tools in the designing process and the modular structure of the exoskeleton will allow the construction of personalized devices using 3D printing. The exoskeleton was designed to work under independent joint control so that the system will be able to work as passive, assistive and active-assistive mode, to keep records of motion for data analysis and to support the rehabilitation process

    A Computational Approach for Human-like Motion Generation in Upper Limb Exoskeletons Supporting Scapulohumeral Rhythms

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    This paper proposes a computational approach for generation of reference path for upper-limb exoskeletons considering the scapulohumeral rhythms of the shoulder. The proposed method can be used in upper-limb exoskeletons with 3 Degrees of Freedom (DoF) in shoulder and 1 DoF in elbow, which are capable of supporting shoulder girdle. The developed computational method is based on Central Nervous System (CNS) governing rules. Existing computational reference generation methods are based on the assumption of fixed shoulder center during motions. This assumption can be considered valid for reaching movements with limited range of motion (RoM). However, most upper limb motions such as Activities of Daily Living (ADL) include large scale inward and outward reaching motions, during which the center of shoulder joint moves significantly. The proposed method generates the reference motion based on a simple model of human arm and a transformation can be used to map the developed motion for other exoskeleton with different kinematics. Comparison of the model outputs with experimental results of healthy subjects performing ADL, show that the proposed model is able to reproduce human-like motions.Comment: In 2017 IEEE International Symposium on Wearable & Rehabilitation Robotics (WeRob2017

    Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking

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    This manuscript presents control of a high-DOF fully actuated lower-limb exoskeleton for paraplegic individuals. The key novelty is the ability for the user to walk without the use of crutches or other external means of stabilization. We harness the power of modern optimization techniques and supervised machine learning to develop a smooth feedback control policy that provides robust velocity regulation and perturbation rejection. Preliminary evaluation of the stability and robustness of the proposed approach is demonstrated through the Gazebo simulation environment. In addition, preliminary experimental results with (complete) paraplegic individuals are included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses reviewers' concerns about the robustness of the algorithm and the motivation for using such exoskeleton

    A flexible sensor technology for the distributed measurement of interaction pressure

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    We present a sensor technology for the measure of the physical human-robot interaction pressure developed in the last years at Scuola Superiore Sant'Anna. The system is composed of flexible matrices of opto-electronic sensors covered by a soft silicone cover. This sensory system is completely modular and scalable, allowing one to cover areas of any sizes and shapes, and to measure different pressure ranges. In this work we present the main application areas for this technology. A first generation of the system was used to monitor human-robot interaction in upper- (NEUROExos; Scuola Superiore Sant'Anna) and lower-limb (LOPES; University of Twente) exoskeletons for rehabilitation. A second generation, with increased resolution and wireless connection, was used to develop a pressure-sensitive foot insole and an improved human-robot interaction measurement systems. The experimental characterization of the latter system along with its validation on three healthy subjects is presented here for the first time. A perspective on future uses and development of the technology is finally drafted

    Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation

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    Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration

    Feature Analysis for Classification of Physical Actions using surface EMG Data

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    Based on recent health statistics, there are several thousands of people with limb disability and gait disorders that require a medical assistance. A robot assisted rehabilitation therapy can help them recover and return to a normal life. In this scenario, a successful methodology is to use the EMG signal based information to control the support robotics. For this mechanism to function properly, the EMG signal from the muscles has to be sensed and then the biological motor intention has to be decoded and finally the resulting information has to be communicated to the controller of the robot. An accurate detection of the motor intention requires a pattern recognition based categorical identification. Hence in this paper, we propose an improved classification framework by identification of the relevant features that drive the pattern recognition algorithm. Major contributions include a set of modified spectral moment based features and another relevant inter-channel correlation feature that contribute to an improved classification performance. Next, we conducted a sensitivity analysis of the classification algorithm to different EMG channels. Finally, the classifier performance is compared to that of the other state-of the art algorithm

    Development of an exoskeleton robot for upper-limb rehabilitation

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    To assist or rehabilitate individuals with impaired upper-limb function, we have developed an upper-limb exoskeleton robot, the ETS-MARSE (motion assistive robotic-exoskeleton for superior extremity). The MARSE is comprised of a shoulder motion support part, an elbow and forearm motion support part, and a wrist motion support part. It is designed to be worn on the lateral side of the upper limb in order to provide naturalistic movements of the shoulder (i.e., vertical and horizontal flexion/extension, and internal/external rotation), elbow (i.e., flexion/extension), forearm (i.e., pronation/supination), and wrist joint (i.e., radial/ulnar deviation, and flexion/extension). This thesis focuses on the modeling, design (mechanical and electrical components), development, and control of the developed MARSE. The proposed MARSE was modeled based on the upper-limb biomechanics; it has a relatively low weight, an excellent power/weight ratio, can be easily fitted or removed, and is able to effectively compensate for gravity. Moreover, to avoid complex cable routing that could be found in many exoskeleton systems, a novel power transmission mechanism was introduced for assisting shoulder joint internal/external rotation and for forearm pronation/supination. The exoskeleton was designed for use by typical adults. However, provisions are included for link length adjustments to accommodate a wide range of users. The entire exoskeleton arm was fabricated primarily in aluminum except the high stress joint sections which were fabricated in mild steel to give the exoskeleton structure a relatively light weight. Brushless DC motors (incorporated with Harmonic Drives) were used to actuate the developed MARSE. The kinematic model of the MARSE was developed based on modified Denavit-Hartenberg notations. In dynamic modeling and control, robot parameters such as robot arm link lengths, upper-limb masses, and inertia, are estimated according to the upper limb properties of a typical adult. Though the exoskeleton was developed with the goal of providing different forms of rehab therapy (namely passive arm movements, active-assisted therapy, and resistive therapy), this research concentrated only on passive form of rehabilitation. Passive arm movements and exercises are usually performed slowly compared to the natural speed of arm movement. Therefore, to control the developed MARSE, a computationally inexpensive a PID controller and a PID-based compliance controller were primarily employed. Further, realizing the dynamic modeling of human arm movement which is nonlinear in nature, a nonlinear computed torque control (CTC) and a modified sliding mode exponential reaching law (mSMERL) techniques were employed to control the MARSE. Note that to improve transient tracking performance and to reduce chattering, this thesis proposed the mSMERL, a novel nonlinear control strategy that combined the concept of boundary layer technique and the exponential reaching law. The control architecture was implemented on a field-programmable gate array (FPGA) in conjunction with a RT-PC. In experiments, typical rehabilitation exercises for single and multi joint movements (e.g., reaching) were performed. Experiments were carried out with healthy human subjects where trajectories (i.e., pre-programmed trajectories recommended by therapist/clinician) tracking the form of passive rehabilitation exercises were carried out. This thesis also focused on the development of a 7DoFs upper-limb prototype (lower scaled) ‘master exoskeleton arm’ (mExoArm). Furthermore, experiments were carried out with the mExoArm where subjects (robot users) operate the mExoArm (like a joystick) to maneuver the MARSE to provide passive rehabilitation. Experimental results show that the developed MARSE can effectively perform passive rehabilitation exercises for shoulder, elbow and wrist joint movements. Using mExoArm offers users some flexibility over pre-programmed trajectories selection approach, especially in choosing range of movement and speed of motion. Moreover, the mExoArm could potentially be used to tele-operate the MARSE in providing rehabilitation exercises
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