A neural tracking and motor control approach to improve rehabilitation of upper limb movements

<p>Abstract</p> <p>Background</p> <p>Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an <it>ad hoc </it>markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.</p> <p>Methods</p> <p>The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.</p> <p>Results</p> <p>The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.</p> <p>Conclusion</p> <p>A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.</p

Mobility in individuals with traumatic lower-limb injuries : implications for device design, surgical intervention and rehabilitation therapies

Traumatic injuries to the extremities are commonly observed in emergency room patients and military personnel in combat. Restoring high mobility and functionality is a primary goal post-injury, which may require the use of rehabilitative devices, surgical interventions, and rehabilitation therapies. The research detailed in this dissertation investigates specific elements of these approaches through the use of experimental study and modeling and simulation. In the first study, the influence of passive-dynamic ankle-foot orthosis bending axis on the gait performance of limb salvage subjects was investigated. Bending axis location was altered by fabricating customized orthosis components using additive manufacturing and was tested in a gait laboratory. Altering bending axis location did not result in large or consistent changes in gait measures, however subjects expressed strong preferences for bending axis condition and preference was strongly related to specific gait measures. This suggests that preference and comfort are important factors guiding the prescription of bending axis location. In the second study, musculoskeletal modeling was used to examine the influence of transfemoral amputation surgical techniques on muscle capacity to generate forces and moments about the hip. Muscle reattachment tension and stabilization were shown to be critical parameters for post-amputation capacity, which supports the use of myodesis stabilization (muscle is reattached directly to bone) in amputation procedures. In the third study, a forward dynamics simulation of transfemoral amputee gait was developed and used to examine individual muscle and prosthesis contributions to walking subtasks. The residual hip muscles, and intact ankle, knee, and hip muscles worked synergistically to provide body support, anteroposterior propulsion, mediolateral control, and leg swing. Increased contributions of contralateral muscles to ipsilateral subtasks as well as increased duration of specific muscle contributions were observed in comparison to non-amputee and transtibial amputee walking. These findings can be used to help develop targeted rehabilitation therapies and improve transfemoral amputee locomotion. Through elucidating the influence of PD-AFO bending axis on gait performance as well as the influence of transfemoral amputation surgical techniques on muscle capacity and function, this research provides a foundation for improved rehabilitation outcomes, and thus mobility for individuals who have experienced traumatic lower-limb injuries.Mechanical Engineerin

PREDICTIVE SIMULATION OF HUMAN MOVEMENT AND APPLICATIONS TO ASSISTIVE DEVICE DESIGN AND CONTROL

Predictive simulation based on dynamic optimization using musculoskeletal models is a powerful approach for studying biomechanics of human gait. Predictive simulation can be used for a variety of applications from designing assistive devices to testing theories of motor controls. However, one of the challenges in formulating the predictive dynamic optimization problem is that the cost function, which represents the underlying goal of the walking task (e.g., minimal energy consumption) is generally unknown and is assumed a priori. While different studies used different cost functions, the qualities of the gaits with those cost functions were often not provided. Therefore, this dissertation evaluates and examines different cost function forms for dynamic simulation of human walking. The problem of the walking cost function determination was cast as a bilevel optimization, which was solved using a nested evolutionary approach. The results showed cost functions based on a weighted combination of muscle-based performance criteria (e.g., metabolic cost, muscle fatigue), gait smoothness, and gait stability led to better walking solutions compared to any cost functions only based on muscle performance criteria. Further evaluations of the walking cost functions were done in the simulation cases of human walking augmented with assistive devices such as prosthesis and exoskeleton. The simulations of augmented walking were comparable to the experimental results, which suggests the potential of using the simulation approach to address problems of finding assistive device design and control

Generating Optimal Control Simulations of Musculoskeletal Movement using OpenSim and MATLAB

Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tools for generating tracking simulations but not predictive simulations. However, OpenSim includes an extensive application programming interface that permits extending its capabilities with scripting languages such as MATLAB. In the work presented here, we combine the computational tools provided by MATLAB with the musculoskeletal modeling capabilities of OpenSim to create a framework for generating predictive simulations of musculoskeletal movement based on direct collocation optimal control techniques. In many cases, the direct collocation approach can be used to solve optimal control problems considerably faster than traditional shooting methods. Cyclical and discrete movement problems were solved using a simple 1 degree of freedom musculoskeletal model and a model of the human lower limb, respectively. The problems could be solved in reasonable amounts of time (several seconds to 1–2 hours) using the open-source IPOPT solver. The problems could also be solved using the fmincon solver that is included with MATLAB, but the computation times were excessively long for all but the smallest of problems. The performance advantage for IPOPT was derived primarily by exploiting sparsity in the constraints Jacobian. The framework presented here provides a powerful and flexible approach for generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB. This should allow researchers to more readily use predictive simulation as a tool to address clinical conditions that limit human mobility

Model-based myoelectric control of robots for assistance and rehabilitation

The first anthropomorphic robots and exoskeletons were developed with the idea of combining man and machine into an intimate symbiotic unit that can perform as one joint system. A human-robot interface consists of processes of two different nature: (1) the physical interaction (pHRI) between the device and its user and (2) the exchange of cognitive information (cHRI) between the human and the robot. To achieve the symbiosis between the two actors, both need to be optimized. The evolution of mechanical design and the introduction of new materials pushed pHRI to new frontiers on ergonomics and assistance performance. However, cHRI still lacks on this direction because is more complicated: it requires communication from the cognitive processes occuring in the human agent to the robot, e.g. intention detection; but also from the robot to the human agent, e.g. feedback modalities such as haptic cues. A possible innovation is the inclusion of the electromyographic signal, the command signal from our brain to the musculoskeletal system for the movement, in the robot control loop. The aim of this thesis was to develop a real-time control framework for an assistive device that can generate the same force produced by the muscles. To do this, I incorporated in the robot control loop a detailed musculoskeletal model that estimates the net torque at the joint level by taking as inputs the electromyography signals and kinematic data. This module is called myoprocessor. Here I present two applications of this control approach: the first was implemented on a soft wearable arm exosuit in order to evaluate the adaptation of the controller on different motion and loads. The second one, was a generation of myoprocessor-driven force field on a planar robot manipulandum in order to study the modularity changes of the musculoskeletal system. Both applications showed that the device controlled by myoprocessor works symbiotically with the user, by reducing the muscular activity and preserving the motor performance. The ability of seamlessly combining musculoskeletal force estimators with assistive devices opens new avenues for assisting human movement both in healthy and impaired individuals

Ankle-Foot Orthosis Stiffness: Biomechanical Effects, Measurement and Emulation

Ankle-foot orthoses (AFOs) are braces worn by individuals with gait impairments to provide support about the ankle. AFOs come in a variety of designs for clinicians to choose from. However, as the effects of different design parameters on AFO properties and AFO users have not been adequately quantified, it is not clear which design choices are most likely to improve patient outcomes. Recent advances in manufacturing have further expanded the design space, adding urgency and complexity to the challenge of selecting optimal designs. A key AFO property affected by design decisions is sagittal-plane rotational stiffness. To evaluate the effectiveness of different AFO designs, we need: 1) a better understanding of the biomechanical effects of AFO stiffness and 2) more precise and repeatable stiffness measurement methods. This dissertation addresses these needs by accomplishing four aims. First, we conducted a systematic literature review on the influence of AFO stiffness on gait biomechanics. We found that ankle and knee kinematics are affected by increasing stiffness, with minimal effects on hip kinematics and kinetics. However, the lack of effective stiffness measurement techniques made it difficult to determine which specific values or ranges of stiffness influence biomechanics. Therefore, in Aim2, we developed an AFO stiffness measurement apparatus (SMApp). The SMApp is an automated device that non-destructively flexes an AFO to acquire operator- and trial-independent measurements of its torque-angle dynamics. The SMApp was designed to test a variety of AFO types and sizes across a wide range of flexion angles and speeds exceeding current alternatives. Common models of AFO torque-angle dynamics in literature have simplified the relationship to a linear fit whose slope represents stiffness. This linear approximation ignores damping parameters. However, as previous studies were unable to precisely control AFO flexion speed, the presence of speed effects has not been adequately investigated. Thus, in Aim3, we used the SMApp to test whether AFOs exhibit viscoelastic behaviors over the range of speeds typically achieved during walking. This study revealed small but statistically significant effects of flexion speed on AFO stiffness for samples of both traditional AFOs and novel 3-D printed AFOs, suggesting that more complex models that include damping parameters could be more suitable for modeling AFO dynamics. Finally, in Aim 4, we investigated the use of an active exoskeleton, that can haptically-emulate different AFOs, as a potential test bed for studying the effects of AFO parameters on human movement. Prior work has used emulation for rapid prototyping of candidate assistive devices. While emulators can mimic a physical device's torque-angle profile, the physical and emulated devices may have other differences that influence user biomechanics. Current studies have not investigated these differences, which limits translation of findings from emulated to physical devices. To evaluate the efficacy of AFO emulation as a research tool, we conducted a single-subject pilot study with a custom-built AFO emulator device. We compared user kinematics while walking with a physical AFO against those with an emulated AFO and found they elicited similar ankle trajectories. This dissertation resulted in the successful development and evaluation of a framework consisting of two test beds, one to assess AFO mechanical properties and another to assess the effects of these properties on the AFO user. These tools enable innovations in AFO design that can translate to measurable improvements in patient outcomes.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163219/1/deema_1.pd

Intelligent Controls for a Semi-Active Hydraulic Prosthetic Knee

We discuss open loop control development and simulation results for a semi-active above-knee prosthesis. The control signal consists of two hydraulic valve settings. These valves control a rotary actuator that provides torque to the prosthetic knee. We develop open loop control using biogeography-based optimization (BBO), which is a recently developed evolutionary algorithm, and gradient descent. We use gradient descent to show that the control generated by BBO is locally optimal. This research contributes to the field of evolutionary algorithms by demonstrating that BBO is successful at finding optimal solutions to complex, real-world, nonlinear, time varying control problems. The research contributes to the field of prosthetics by showing that it is possible to find effective open loop control signals for a newly proposed semi-active hydraulic knee prosthesis. The control algorithm provides knee angle tracking with an RMS error of 7.9 degrees, and thigh angle tracking with an RMS error of 4.7 degrees. Robustness tests show that the BBO control solution is affected very little by disturbances added during the simulation. However, the open loop control is very sensitive to the initial conditions. So a closed loop control is needed to mitigate the effects of varying initial conditions. We implement a proportional, integral, derivative (PID) controller for the prosthesis and show that it is not a sufficient form of closed loop control. Instead, we implement artificial neural networks (ANNs) as the mechanism for closed loop control. We show that ANNs can greatly improve performance when noise and disturbance cause high tracking errors, thus reducing the risk of stumbles and falls. We also show that ANNs are able to improve average performance by as much as 8 over open loop control. We also discuss embedded system implementation with a microcontroller and associated hardware and softwar

Validation of an Accelerometry Based Method of Human Gait Analysis

Gait analysis is the quantification of locomotion. Understanding the science behind the way we move is of interest to a wide variety of fields. Medical professionals might use gait analysis to track the rehabilitation progress of a patient. An engineer may want to design wearable robotics to augment a human operator. Use cases even extend into the sport and entertainment industries. Typically, a gait analysis is performed in a highly specialized laboratory containing cumbersome expensive equipment. The process is tedious and requires specially trained operators. Continued development of small and cheap inertial measurement units (IMUs) over an alternative to current methods of gait analysis. These devices are portable and simple to use allowing gait analysis to be done outside the laboratory in real world environments. Unfortunately, while current IMU based gait analysis systems are able to quantify a subject\u27s joint kinematics they are unable to measure joint kinetics as could be done in a traditional gait laboratory. A novel musculoskeletal model-based movement analysis system using accelerometers has been developed that can calculate both joint kinematics and joint kinetics. The aim of this master\u27s thesis is to validate this accelerometer based gait analysis against the industry standard optical motion capture gait analysi