RUPERT closed loop control design

Exerciser for rehabilitation of the Arm (ERA): Development and unique features of a 3D end-effector robot

Stroke is one of the leading causes of disability worldwide. Consequently, many stroke survivors exhibit difficulties undergoing voluntary movement in their affected upper limb, compromising their functional performance and level of independence. To minimize the negative impact of stroke disabilities, exercises are recognized as a key element in post-stroke rehabilitation. In order to provide the practice of exercises in a uniform and controlled manner as well as increasing the efficiency of therapists' interventions, robotic training has been found, and continues to prove itself, as an innovative intervention for post-stroke rehabilitation. However, the complexity as well as the limited degrees of freedom and workspace of currently commercially available robots can limit their use in clinical settings. Up to now, user-friendly robots covering a sufficiently large workspace for training of the upper limb in its full range of motion are lacking. This paper presents the design and implementation of ERA, an upper-limb 3-DOF force-controlled exerciser robot, which presents a workspace covering the entire range of motion of the upper limb. The ERA robot provides 3D reaching movements in a haptic virtual environment. A description of the hardware and software components of the ERA robot is also presented along with a demonstration of its capabilities in one of the three operational modes that were developed.

Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR.

Inverse Kinematics for Upper Limb Compound Movement Estimation in Exoskeleton-Assisted Rehabilitation

Robot-Assisted Rehabilitation (RAR) is relevant for treating patients affected by nervous system injuries (e.g., stroke and spinal cord injury). The accurate estimation of the joint angles of the patient limbs in RAR is critical to assess the patient improvement. The economical prevalent method to estimate the patient posture in Exoskeleton-based RAR is to approximate the limb joint angles with the ones of the Exoskeleton. This approximation is rough since their kinematic structures differ. Motion capture systems (MOCAPs) can improve the estimations, at the expenses of a considerable overload of the therapy setup. Alternatively, the Extended Inverse Kinematics Posture Estimation (EIKPE) computational method models the limb and Exoskeleton as differing parallel kinematic chains. EIKPE has been tested with single DOF movements of the wrist and elbow joints. This paper presents the assessment of EIKPE with elbow-shoulder compound movements (i.e., object prehension). Ground-truth for estimation assessment is obtained from an optical MOCAP (not intended for the treatment stage). The assessment shows EIKPE rendering a good numerical approximation of the actual posture during the compound movement execution, especially for the shoulder joint angles. This work opens the horizon for clinical studies with patient groups, Exoskeleton models, and movements types.

A survey on robotic devices for upper limb rehabilitation

The existing shortage of therapists and caregivers assisting physically disabled individuals at home is expected to increase and become serious problem in the near future. The patient population needing physical rehabilitation of the upper extremity is also constantly increasing. Robotic devices have the potential to address this problem as noted by the results of recent research studies. However, the availability of these devices in clinical settings is limited, leaving plenty of room for improvement. The purpose of this paper is to document a review of robotic devices for upper limb rehabilitation including those in developing phase in order to provide a comprehensive reference about existing solutions and facilitate the development of new and improved devices. In particular the following issues are discussed: application field, target group, type of assistance, mechanical design, control strategy and clinical evaluation. This paper also includes a comprehensive, tabulated comparison of technical solutions implemented in various systems.

Feasibility studies of robot-assisted stroke rehabilitation at clinic and home settings using RUPERT

Task based repetitive therapy has been proposed to help stroke survivors to regain functional control of arm movement. We developed a wearable exoskeleton rehabilitation robot with associated control algorithm and safety protection mechanisms, and a graphic user interface that is easy to use and intuitive to patients and therapists, as the framework for automated and customizable robot-assisted rehabilitation system for clinic and home based therapy. The system was tested in two feasibility studies. The first study involved 6 patients to receive therapeutic training during three time weekly clinic visits for 4 weeks. The second study set up the robot-assisted rehabilitation system at patient's house, where the therapeutic training was practiced on a daily base. Two patients were recruited for the home application study. Patients' performances were assessed using clinical evaluation tools, including Wolf Motor Function Test and Fugl Meyer Assessment (FMA), both before and after the training. The performances of patients during the training weeks were also objectively evaluated by using the robot sensory data.

Pneumatic robotic systems for upper limb rehabilitation

The aim of rehabilitation robotic area is to research on the application of robotic devices to therapeutic procedures. The goal is to achieve the best possible motor, cognitive and functional recovery for people with impairments following various diseases. Pneumatic actuators are attractive for robotic rehabilitation applications because they are lightweight, powerful, and compliant, but their control has historically been difficult, limiting their use. This article first reviews the current state-of-art in rehabilitation robotic devices with pneumatic actuation systems reporting main features and control issues of each therapeutic device. Then, a new pneumatic rehabilitation robot for proprioceptive neuromuscular facilitation therapies and for relearning daily living skills: like taking a glass, drinking, and placing object on shelves is described as a case study and compared with the current pneumatic rehabilitation devices.