Eric T. Wolbrecht

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 patients 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.

Comparison of three-dimensional, assist-as-needed robotic arm/hand movement training provided with Pneu-WREX to conventional tabletop therapy after chronic stroke.

ObjectivesRobot-assisted movement training can help individuals with stroke reduce arm and hand impairment, but robot therapy is typically only about as effective as conventional therapy. Refining the way that robots assist during training may make them more effective than conventional therapy. Here, the authors measured the therapeutic effect of a robot that required individuals with a stroke to achieve virtual tasks in three dimensions against gravity. DesignThe robot continuously estimated how much assistance patients needed to perform the tasks and provided slightly less assistance than needed to reduce patient slacking. Individuals with a chronic stroke (n = 26; baseline upper limb Fugl-Meyer score, 23 ± 8) were randomized into two groups and underwent 24 one-hour training sessions over 2 mos. One group received the assist-as-needed robot training and the other received conventional tabletop therapy with the supervision of a physical therapist. ResultsTraining helped both groups significantly reduce their motor impairment, as measured by the primary outcome measure, the Fugl-Meyer score, but the improvement was small (3.0 ± 4.9 points for robot therapy vs. 0.9 ± 1.7 for conventional therapy). There was a trend for greater reduction for the robot-trained group (P = 0.07). The robot group largely sustained this gain at the 3-mo follow-up. The robot-trained group also experienced significant improvements in Box and Blocks score and hand grip strength, whereas the control group did not, but these improvements were not sustained at follow-up. In addition, the robot-trained group showed a trend toward greater improvement in sensory function, as measured by the Nottingham Sensory Test (P = 0.06). ConclusionsThese results suggest that in patients with chronic stroke and moderate-severe deficits, assisting in three-dimensional virtual tasks with an assist-as-needed controller may make robotic training more effective than conventional tabletop training.

Biomimetic orthosis for the neurorehabilitation of the elbow and shoulder (BONES)

This paper presents a novel design for a 4 degree of freedom pneumatically-actuated upper-limb rehabilitation device. BONES is based on a parallel mechanism that actuates the upper arm by means of two passive, sliding rods pivoting with respect to a fixed structural frame. Four, mechanically-grounded pneumatic actuators are placed behind the main structural frame to control shoulder motion via the sliding rods, and a fifth cylinder is located on the structure to control elbow flexion/extension. The device accommodates a wide range of motion of the human arm, while also achieving low inertia and direct-drive force generation capability at the shoulder. A key accomplishment of this design is the ability to generate arm internal/external rotation without any circular bearing element such as a ring, a design feature inspired by the biomechanics of the human forearm. The paper describes the rationale for this device and its main design aspects including its kinematics, range of motion, and force generation capability.

Pneumatic Control of Robots for Rehabilitation

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. In this paper we present the pneumatic control system developed for Pneu-WREX: a pneumatically actuated, upper extremity orthosis for rehabilitation after stroke. The developed pneumatic control system combines several novel components to make the entire system stable, reliable, and backdrivable. These components, which are described in this paper, include: (1) a unique two-valve force control subsystem that keeps chamber pressure low (to reduce friction and energy consumption) and adaptively compensates for leakage; (2) a new servovalve characterization approach that uses experimentally measured data in a combined non-linear and least-squares regression to obtain a linear relationship between mass flow and valve voltage; and (3) a new approach to state estimation using accelerometers and a Kalman filter to obtain clean signals for use in a non-linear adaptive feedback control law. Experimental testing of the device demonstrates the efficacy of the developed pneumatic control system.

Control of a Pneumatic Orthosis for Upper Extremity Stroke Rehabilitation

A key challenge in rehabilitation robotics is the development of a lightweight, large force, high degrees-of-freedom device that can assist in functional rehabilitation of the arm. Pneumatic actuators can potentially help meet this challenge because of their high power-to-weight ratio. They are currently not widely used for rehabilitation robotics because they are difficult to control. This paper describes the control development of a pneumatically actuated, upper extremity orthosis for rehabilitation after stroke. To provide the sensing needed for good pneumatic control, position and velocity of the robot are estimated by a unique implementation of a Kalman filter using MEMS accelerometers. To compensate for the nonlinear behavior of the pneumatic servovalves, force control is achieved using a new method for air flow mapping using experimentally measured data in a least-squares regression. To help patients move with an inherently compliant robot, a high level controller that assists only as needed in reaching exercises is developed. This high level controller differs from traditional trajectory-based, position controllers, allowing free voluntary movements toward a target while resisting movements away from the target. When the target cannot be reached voluntarily, the controller slowly builds up force, pushing the arm toward the target. As each target position is reached, the controller builds an internal model of the subjects capability, learning the forces necessary to complete movements. Preliminary testing performed on a non-disabled subject demonstrated the ability of the orthosis to complete reaching movements with graded assistance and to adapt to the effort level of the subject. Thus, the orthosis is a promising tool for upper extremity rehabilitation after stroke

Do robotic and non-robotic arm movement training drive motor recovery after stroke by a common neural mechanism? experimental evidence and a computational model

Different dose-matched, upper extremity rehabilitation training techniques, including robotic and non-robotic techniques, can result in similar improvement in movement ability after stroke, suggesting they may elicit a common drive for recovery. Here we report experimental results that support the hypothesis of a common drive, and develop a computational model of a putative neural mechanism for the common drive. We compared weekly motor control recovery during robotic and unassisted movement training techniques after chronic stroke (n = 27), as assessed with quantitative measures of strength, speed, and coordination. The results showed that recovery in both groups followed an exponential time course with a time constant of about 4–5 weeks. Despite the greater range and speed of movement practiced by the robot group, motor recovery was very similar between the groups. The premise of the computational model is that improvements in motor control are caused by improvements in the ability to activate spared portions of the damaged corticospinal system, as learned by a biologically plausible search algorithm. Robot-assisted and unassisted training would in theory equally drive this search process.

AUV navigation in the presence of a magnetic disturbance with an extended Kalman filter

This paper presents a novel extended Kalman filter (EKF) used for navigation of an autonomous underwater vehicle (AUV). The AUV contains a magnetic compass, which is susceptible to magnetic disturbances, and an angular velocity sensor, which exhibits drift if solely integrated to estimate heading. To address these problems, the presented EKF fuses the information from these sensors in order to produce a more accurate estimate of heading, and learns a heading bias in real-time. The presented method has two distinct advantages. First, the heading bias can correct for errors from a poorly calibrated magnetic heading sensor. Second, the angular velocity information improves heading estimation, especially in the presence of a magnetic disturbance. Because the AUVs presented here are designed to acquire magnetic field measurements, this second advantage is of particular importance. In both simulation and experimental testing, the presented EKF learned a calibration bias for the magnetic heading sensor and improved heading estimation in the presence of a magnetic disturbance.

Single degree-of-freedom exoskeleton mechanism design for finger rehabilitation

This paper presents the kinematic design of a single degree-of-freedom exoskeleton mechanism: a planar eight-bar mechanism for finger curling. The mechanism is part of a finger-thumb robotic device for hand therapy that will allow users to practice key pinch grip and finger-thumb opposition, allowing discrete control inputs for playing notes on a musical gaming interface. This approach uses the mechanism to generate the desired grasping trajectory rather than actuating the joints of the fingers and thumb independently. In addition, the mechanism is confined to the back of the hand, so as to allow sensory input into the palm of the hand, minimal size and apparent inertia, and the possibility of placing multiple mechanisms side-by-side to allow control of individual fingers.

Synchronous navigation of AUVs using WHOI micro-modem 13-bit communications

This paper presents the development and testing of a synchronous one-way travel time (OWTT) navigation system for autonomous underwater vehicles (AUVs). Synchronous OWTT navigation is important for multiple vehicle applications as it preserves the range update rate independent of fleet size. The presented approach is based on the use of ranges generated by 13-bit acoustic messages and an a priori temporal communications cycle. The accuracy of the 13-bit acoustic ranges was experimentally compared to independent ground truth measurements. In addition, the presented synchronous OWTT approach was successfully used for real-time navigation of an AUV in field testing. The results show comparable performance to traditional two-way travel time (TWTT) navigation.

Adaptive control with state-dependent modeling of patient impairment for robotic movement therapy

This paper presents an adaptive control approach for robotic movement therapy that learns a state-dependent model of patient impairment. Unlike previous work, this approach uses an unstructured inertial model that depends on both the position and direction of the desired motion in the robots workspace. This method learns a patient impairment model that accounts for movement specific disability in neuro-muscular output (such as flexion vs. extension and slow vs. dynamic tasks). Combined with assist-as-needed force decay, this approach may promote further patient engagement and participation. Using the robotic therapy device, FINGER (Finger Individuating Grasp Exercise Robot), several experiments are presented to demonstrate the ability of the adaptive control to learn state-dependent abilities.