Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Fast human motion prediction for human-robot collaboration with wearable interfaces

In this paper, we aim at improving human motion prediction during human-robot collaboration in industrial facilities by exploiting contributions from both physical and physiological signals. Improved human-machine collaboration could prove useful in several areas, while it is crucial for interacting robots to understand human movement as soon as possible to avoid accidents and injuries. In this perspective, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The proposed interface can find many applications in the Industry 4.0 framework, where autonomous and collaborative robots will be an essential part of innovative facilities. A motion intention prediction and a motion direction prediction levels have been developed to improve detection speed and accuracy. A Gaussian Mixture Model (GMM) has been trained with IMU and EMG data following an evidence accumulation approach to predict reaching direction. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy according to the application. The output of the two predictors has been used as external inputs to a Finite State Machine (FSM) to control the behaviour of a physical robot according to user's action or inaction. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of $94.3\pm2.9\%$ after $160.0msec\pm80.0msec$ from movement onset

Doctor of Philosophy

dissertationMost humans have difficulty performing precision tasks, such as writing and painting, without additional physical support(s) to help steady or offload their arm's weight. To alleviate this problem, various passive and active devices have been developed. However, such devices often have a small workspace and lack scalable gravity compensation throughout the workspace and/or diversity in their applications. This dissertation describes the development of a Spatial Active Handrest (SAHR), a large-workspace manipulation aid, to offload the weight of the user's arm and increase user's accuracy over a large three-dimensional workspace. This device has four degrees-of-freedom and allows the user to perform dexterous tasks within a large workspace that matches the workspace of a human arm when performing daily tasks. Users can move this device to a desired position and orientation using force or position inputs, or a combination of both. The SAHR converts the given input(s) to desired velocit

THE EFFECT OF THE MYOMO ROBOTIC ORTHOSIS ON REACH PERFORMANCE AFTER STROKE

Stroke affects 795,000 people yearly. Close to 85 percent of stroke survivors experience some degree of stroke-related upper extremity impairment due to spastic paresis (Nakayama, Jorgenson, Pedersen, Raaschou, & Olsen, 1994). Residual upper extremity impairments are associated with increased burden of care (Skidmore, Rogers, Chandler, & Holm, 2006) and decreased ability to garner gainful employment (Desrosiers et al., 2006). Current best evidence supports the use of a task-oriented practice regimen for the treatment of upper extremity impairment; however, many people have insufficient motor control to participate. It was the goal of this study to investigate the effect of the Myomo robotic upper extremity orthosis, a device that facilitates participation in a task-oriented practice regimen, on reach kinematic performance. Specifically, we examined two research questions: Question 1. What is the immediate effect of the Myomo orthosis on kinematic performance of reach? We predicted that before training, temporal (movement efficiency) and spatial characteristics (angular displacement, movement error, and acceleration cycles) of kinematic performance would be better with the Myomo orthosis than without the device. Question 2. What is the training effect of the Myomo orthosis plus training kinematic performance? We predicted that temporal (movement efficiency) and spatial characteristics (angular displacement, movement error, and acceleration cycles) of kinematic performance without the Myomo orthosis would be better after 16 training sessions. Findings suggest that the immediate effect of the Myomo orthosis on reaching performance (question 1) appears to be more attenuated than the training effect of the Myomo orthosis (question 2).All 6 participants demonstrated improvements in movement efficiency for one or more of three reaching targets. Five of the 6 participants demonstrated improvements in one or more of the spatial characteristics of kinematic performance. Effect size calculations suggest that the magnitude of the training effect was greatest for movement efficiency and angular displacement (medium effect size) and the least for movement error and acceleration cycles (small effect size)

A Comparative Analysis of Speed Profile Models for Ankle Pointing Movements: Evidence that Lower and Upper Extremity Discrete Movements are Controlled by a Single Invariant Strategy

Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cerebral palsy highlighted the importance of analyzing and modeling the kinematics of the lower limbs, in general, and those of the ankle joints, in particular. We recruited 15 young healthy adults that performed in total 1,386 visually evoked, visually guided, and target-directed discrete pointing movements with their ankle in dorsal–plantar and inversion–eversion directions. Using a non-linear, least-squares error-minimization procedure, we estimated the parameters for 19 models, which were initially designed to capture the dynamics of upper limb movements of various complexity. We validated our models based on their ability to reconstruct the experimental data. Our results suggest a remarkable similarity between the top-performing models that described the speed profiles of ankle pointing movements and the ones previously found for the UE both during arm reaching and wrist pointing movements. Among the top performers were the support-bounded lognormal and the beta models that have a neurophysiological basis and have been successfully used in upper extremity studies with normal subjects and patients. Our findings suggest that the same model can be applied to different “human” hardware, perhaps revealing a key invariant in human motor control. These findings have a great potential to enhance our rehabilitation efforts in any population with lower extremity deficits by, for example, assessing the level of motor impairment and improvement as well as informing the design of control algorithms for therapeutic ankle robots

Doctor of Philosophy

dissertationHumans generally have difficulty performing precision tasks with their unsupported hands. To compensate for this difficulty, people often seek to support or rest their hand and arm on a fixed surface. However, when the precision task needs to be performed over a workspace larger than what can be reached from a fixed position, a fixed support is no longer useful. This dissertation describes the development of the Active Handrest, a device that expands its user's dexterous workspace by providing ergonomic support and precise repositioning motions over a large workspace. The prototype Active Handrest is a planar computer-controlled support for the user's hand and arm. The device can be controlled through force input from the user, position input from a grasped tool, or a combination of inputs. The control algorithm of the Active Handrest converts the input(s) into device motions through admittance control where the device's desired velocity is calculated proportionally to the input force or its equivalent. A robotic 2-axis admittance device was constructed as the initial Planar Active Handrest, or PAHR, prototype. Experiments were conducted to optimize the device's control input strategies. Large workspace shape tracing experiments were used to compare the PAHR to unsupported, fixed support, and passive moveable support conditions. The Active Handrest was found to reduce task error and provide better speedaccuracy performance. Next, virtual fixture strategies were explored for the device. From the options considered, a virtual spring fixture strategy was chosen based on its effectiveness. An experiment was conducted to compare the PAHR with its virtual fixture strategy to traditional virtual fixture techniques for a grasped stylus. Virtual fixtures implemented on the Active Handrest were found to be as effective as fixtures implemented on a grasped tool. Finally, a higher degree-of-freedom Enhanced Planar Active Handrest, or E-PAHR, was constructed to provide support for large workspace precision tasks while more closely following the planar motions of the human arm. Experiments were conducted to investigate appropriate control strategies and device utility. The E-PAHR was found to provide a skill level equal to that of the PAHR with reduced user force input and lower perceived exertion

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

Kinematic and Kinetic Comparisons of Arm and Hand Reaching Movements with Mild and Moderate Gravity-Supported, Computer-Enhanced Armeo®spring: A Case Study

Background: Stroke has been recognized as a leading cause of serious long-term disability in the United States (U.S.) with 795,000 people experience a new or recurrent stroke each year (Roger et al., 2011). The most apparent defect after stroke is motor impairments (Masiero, Armani, & Rosati, 2011). Statistically, half of stroke survivors suffer from upper extremity hemiparesis and approximately one quarter become dependent in activities of daily living (Sanchez et al., 2006). There is strong evidence that intensity and task specificity are the main drivers in an effective treatment program after stroke. In addition, this training should be repetitive, functional, meaningful, and challenging for a patient (Van Peppen et al., 2004). The use of robotic systems to complement standard poststroke multidisciplinary programs is a recent approach that looks very promising. Robotic devices can provide high-intensity, repetitive, task-specific, interactive treatment of the impaired limb and can monitor patients\u27 motor progress objectively and reliably, measuring changes in quantitative movement kinematics and forces (Masiero, Armani, & Rosati, 2011). Objective: The purpose of this study was to examine the role of Armeo®Spring (Hocoma, Inc.), a gravity-supported, computer-enhanced robotic devise, on reaching movements while using two different gravity-support levels (mild and moderate weight support) on individuals with stroke. Methods: One stroke subject and one gender-matched healthy control participated in this study after gaining their informed consent. Both subjects performed a computer-based game (picking apples successfully and placing them in a shopping cart) under two gravity weight-support conditions (mild and moderate) provided by the Armeo®Spring device. The game tasks were described as a reaching cycle which consisted of five phases (initiation, reaching, grasping, transporting, and releasing). Joint angles for the glenohumeral and elbow joints throughout the reaching cycle were found. Three kinematic parameters (completion time, moving velocity, acceleration) and one kinetic parameter (vertical force acting on the forearm) was calculated for various instances and phases of the reaching motion. In addition, the muscle activation patterns for anterior deltoid, middle deltoid, biceps, triceps, extensor digitorum, flexor digitorum, and brachioradialis were found and the mean magnitude of the electromyography (EMG) signal during each phase of the reaching cycle was found as a percentage of the subject\u27s maximum voluntary contraction (MVC). Results: Within the healthy control subject, results demonstrated no significant differences in mean completion time, moving velocity, or acceleration between mild to moderate gravity-support levels during all phases of the cycle. The stroke subject results revealed a significant decrease in the cycle mean completion time (p= 0.042) between the two gravity-support levels, specifically in mean completion time of the grasping phase. A significant increase was found in the initiation phase moving velocity (p=0.039) and a significant decrease was found in the grasping phase (p=0.048) between two gravity-support levels in the stroke subject. Between subjects, significant increase in the cycle mean completion time was found under both mild and moderate conditions (p\u3c.001 for both conditions). Additionally, significant decreases in the moving velocities were found in all phases of the cycle between the healthy control and the stroke subject under both conditions. With increasing weight support, the healthy control subject showed an increase in abduction and flexion degrees at the glenohumeral joint level, and an increase in flexion degrees of the elbow joint. On the other hand, the stroke subject showed a decrease in abduction degrees and an increase in flexion degrees at the glenohumeral joint level, and a decrease in flexion degrees of the elbow joint after increasing the weight-support level. Results demonstrated an increase in the mean of vertical forces when changing gravity-support levels from mild to moderate during all phases of the cycle in both stroke and healthy subjects. Last, the average EMG magnitude during the reaching cycle phases was reduced for muscles acting against gravity (anterior deltoid, middle deltoid, biceps, and brachioradialis) in both the healthy control and the stroke subject. Conclusion: The significant differences in movement performance between mild and moderate physical weight support suggested a preliminary result that the gravity-supported mechanism provides a mean to facilitate functional upper limb motor performance in individuals with stroke. Future studies should examine such effects with larger sample sizes