Decoding motor neuron behavior for advanced control of upper limb prostheses

One of the main challenges in upper limb prosthesis control to date is to provide devices intuitive to use and capable to reproduce the natural movements of the arm and hand. One approach to solve this challenge is to use the same control signals for prosthesis control that our nervous system uses to control its muscles. This thesis aims to investigate the possibility of natural, intuitive prosthesis control using neural information obtained with available surface EMG decomposition methods. In order to explore all aspects of such a novel approach, a series of five studies were performed with the final goal of implementing a proof of concept and comparing its performance with state of the art myoelectric control. The performed investigations revealed important insights in motor unit physiology after targeted muscle reinnervation, EMG decomposition in dynamic voluntary contractions of the forearm, and the properties and challenges of neural information based prosthesis control. The main outcome of the thesis is that neural information based prosthesis control is capable to outperform myoelectric approaches in pattern recognition, linear regression and nonlinear regression, as determined by offline performance comparisons. The final proof of concept for this novel approach was a robust regression method based on neuromusculoskeletal modeling. The kinematics estimation of the proposed approach outperformed EMG-based nonlinear regression in both able-bodied subjects and patients with limb deficiency, indicating that using neural information is a promising avenue for advanced myoelectric control.2017-11-3

Studio dell'interazione tra Sistema Muscoloscheletrico Umano e Dispositivi di Assistenza Robotici

In the latest years, robotic technologies have been increasingly introduced in rehabilitation with the main purpose of reducing the costs and speeding up the recovery process of patients. However, most of the commercial devices impose a pre-programmed trajectory to the limbs of the patients, who therefore behave in a passive way. Another current major limitation is the inability to accurately evaluate the dynamics of the interaction between the patient and the robotic device. This interaction plays a central role in the mutual modulation of human and robot system behavior with respect of their standalone behavior. In particular, the prediction of the interaction can provide useful information to better design the exoskeleton as well as the rehabilitation treatment. This thesis presents my proposed solution for the development of a simulator able to dynamically simulate at the same time the actuated robot device, the human body, and their emerging physical interaction during the movement cooperation. The main idea behind this solution is to decompose the main system in different levels. I called the proposed solution Multi-Level modeling approach, which is the main topic of this thesis. I proposed the following decomposition: Human, Robot, and Boundary Level. The levels are integrated into a whole system in which each of them addresses specific challenges. The Human Level represents the subject who is wearing, for example, an exoskeleton for the lower limbs. To reach a symbiotic collaboration between the subject and the exoskeleton, the proposed approach has to include the subject's intentions and efforts. Moreover, user's internal transformations provide important information about the internal dynamic parameters modulation due to the external device. The Robot Level consists of the wearable robot system which supports the movements. Our proposed approach includes models of both device mechanics and control strategies. This allows to test different control strategies and find the one that better fits each specific patient's needs and characteristics. The last level is the Boundary Level, which has the main objective to model the human-robot mechanical power transfer, including also the non-idealities (such as dissipative forces), in order to accurately estimate interactions. Challenges emerged during the development of the simulator system were faced, investigating different solutions, and selecting and validating the most promising one. First, I selected a common software platform, able to simultaneously reproduce the dynamic behavior of the three levels. The common software platform allows to build a quite flexible system where different solution could be evaluated simply modifying model parameters. Among different available software, OpenSim was selected because it is well known and used for the dynamic study of human movement. Although OpenSim was well tested in biomechanics, it required a further evaluation as simulator for Robot and Boudary Levels. Performed tests and their motivations are reported in this work. Human internal dynamics parameters are modulated by the influence of the external device. I proposed to monitor this variation, taking into consideration the neural drive sent to the muscles. This can be done by measuring the muscles' electromyographic (EMG) signals, which are the electrical potential generated by muscle cells when they are activated, prior to muscle contraction. These signals can be used as input for a physiologically accurate human musculoskeletal model, to calculate the subject contribution to the movement. As the relation between EMGs and the generated muscle forces and joint moments is not linear, the neuromusculoskeletal model is indeed needed to replicate step-by-step all the internal transformations which occur from the excitation of the muscle to the joint movement. Estimation of the emerging interaction, during the human-robot cooperation, can be performed through an interaction model which is basically a set of contact models. Due to the specific rehabilitation purpose of our work, this contact model needs special attention. I introduced and validated a procedure to calibrate the contact models to improve the accuracy of the estimated interaction forces. One of the problems of using EMG signals is that, in order to acquire them in a non-invasive way, surface electrodes must be used; however, this means that the collected data quality is quite susceptible to the electrodes placements and decay, and to electric and magnetic interferences. In many contexts, such as home rehabilitation, this could be a limitation. An alternative solution to avoid the direct EMG measurement is presented in this work. The idea is that for some repetitive tasks, which are most interesting for rehabilitation, it is possible to substitute the direct data collection with a subject specific model of EMGs. The objective of this work is to provide an effective approach to estimate the emerging interaction during the human-robot movement cooperation. The Multi-Level Modeling approach, presented in this thesis, decomposes this complex problem allowing to find all the required components to realize a whole system able to reach this objective

Neuro-Musculoskeletal Mapping for Man-Machine Interfacing.

We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function

Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling.

BACKGROUND: Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. METHODS: We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. RESULTS: We demonstrated patients' control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. CONCLUSIONS: Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots

Musculoskeletal Modeling of the Human Lower Limb Stiffness for Robotic Applications

This research work presents a physiologically accurate and novel computationally fast neuromusculoskeletal model of the human lower limb stiffness. The proposed computational framework uses electromyographic signals, motion capture data and ground reaction forces to predict the force developed by 43 musculotendon actuators. The estimated forces are then used to compute the musculotendon stiffness and the corresponding joint stiffness. The estimations at each musculotendon unit is constrained to simultaneously satisfy the joint angles and the joint moments of force generated with respect to five degrees of freedom, including: Hip Adduction-Abduction, Hip Flexion-Extension, Hip Internal-External Rotation, Knee Flexion-Extension, and Ankle Plantar-Dorsi Flexion. Advanced methods are used to perform accurate muscle-driven dynamic simulations and to guarantee the dynamic consistency between kinematic and kinetic data. This study presents also the design, simulation and prototyping of a small musculoskeletal humanoid made for replicating the human musculoskeletal structure in an artificial apparatus capable to maintain a quiet standing position using only a completely passive elastic actuation structure. The proposed prototype has a total mass of about 2 kg and its height is 40 cm. It comprises of four segments for each leg and six degrees of freedom, including: Hip Adduction-Abduction, Hip Flexion-Extension, Knee Flexion-Extension, Ankle Plantar-Dorsi Flexion, Ankle Inversion-Eversion, and Toe Flexion-Extension. In order to reconstruct the continuous state space parameters proper of the assembly's control of quiet standing, a hybrid non-linear Extended Kalman Filter based technique is proposed to combine a base-excited inverted pendulum kinematic model of the robot with the discrete-time position measurements. This research work provides effective solutions and readily available software tools to improve the human interaction with robotic assistive devices, advancing the research in neuromusculoskeletal modeling to better understand the mechanisms of actuation provided by human muscles and the rules that govern the lower limb joint stiffness regulation. The obtained results suggest that the neuromusculoskeletal modeling technology can be exploited to address the challenges on the development of musculoskeletal humanoids, new generation human-robot interfaces, motion control algorithms, and intelligent assistive wearable devices capable to effectively ensure a proper dynamic coupling between human and robot

A multilevel framework to measure, model, promote, and enhance the symbiotic cooperation between humans and robotic devices

In the latest decades, the common perception about the role of robotic devices in the modern society dramatically changed. In the early stages of robotics, temporally located in the years of the economic boom, the development of new devices was driven by the industrial need of producing more while reducing production time and costs. The demand was, therefore, for robotic devices capable of substituting the humans in performing simple and repetitive activities. The execution of predefined basic activities in the shortest amount of time, inside carefully engineered and confined environments, was the mission of robotic devices. Beside the results obtained in the industrial sector, a progressive widening of the fields interested in robotics – such as rehabilitation, elderly care, and medicine – led to the current vision of the device role. Indeed, these challenging fields require the robot to be a partner, which works side-by-side with the human. Therefore, the device needs to be capable of actively and efficiently interacting with humans, to provide support and overcome their limits in the execution of shared activities, even in highly unpredictable everyday environments. Highly complex and advanced robots, such as surgical robots, rehabilitation devices, flexible manipulators, and service and companion robots, have been recently introduced into the market; despite their complexity, however, they are still tools to be used to perform, better or faster, very specific tasks. The current open challenge is, therefore, to develop a new generation of symbiotically cooperative robotic partners, adding to the devices the capability to detect, understand, and adapt to the real intentions, capabilities, and needs of the humans. To achieve this goal, a bidirectional information channel shall be built to connect the human and the device. In one direction, the device requires to be informed about the state of its user; in the other direction, the human needs to be informed about the state of the whole interacting system. This work reports the research activities that I conducted during my PhD studies in this research direction. Those activities led to the design, development, and assessment on a real application of an innovative multilevel framework to close the cooperation loop between a human and a robotic device, thus promoting and enhancing their symbiotic interaction. Three main levels have been identified as core elements to close this loop: the measure level, the model level, and the extract/synthesize level. The former aims at collecting experimental measures from the whole interacting system; the second aims at estimating and predicting its dynamic behavior; the last aims at providing quantitative information to both the human and the device about their performances and about how to modify their behavior to improve their interaction symbiosis. Within the measure level, the focus has been concentrated on investigating, critically comparing, and selecting the most suitable and advanced technologies to measure kinematics and dynamics quantities in a portable and minimally intrusive way. Particular attention has been paid to new emerging technologies; moreover, useful protocols and pipelines already recognized as de-facto in other fields have been successfully adapted to fit the needs of the man-machine interaction context. Finally, the design of a new sensor has been started to overcome the lack of tools capable of effectively measuring human-device interaction forces. To implement the model level, a common platform to perform integrated multilevel simulations – i.e. simulations where the device and the human are considered together as interacting entities – has been selected and extensively validated. Furthermore, critical aspects characterizing the modeling of the device, the human, and their interactions have been studied and possible solutions have been proposed. For example, modeling the mechanics and the control within the selected software platform allowed accurate estimations of their behavior. To estimate human behavior, new methodologies and approaches based on anatomical neuromusculoskeletal models have been developed, validated, and released as open-source tools for the community, to allow accurate estimates of both kinematics and dynamics at run-time – i.e. at the same time that the movements are performed. An inverse kinematics approach has been developed and validated to estimate human joint angles from the orientation measurements provided by wearable inertial systems. Additionally, a state of the art neuromusculoskeletal modeling toolbox has been improved and interfaced with the other tools of the multilevel framework, to accurately predict human muscle forces, joint moments, and muscle and joint stiffness from electromyographic and kinematic measures. To estimate and predict the interactions, contact models, parameters optimization procedures, and high-level cooperation strategies have been investigated, developed, and applied. Within the extract/synthesize level, the information provided by the other levels has been combined together to develop informative feedbacks for both the device and the human. In one direction, the device has been provided with control signals defining how to adjust the provided support to comply with the task goals and with the human current capabilities and needs. In the other direction, quantitative feedbacks have been developed to inform the human about task execution performances, task targets, and support provided by the device. This information has been provided to the user as visual feedbacks designed to be both exhaustively informative and minimally distractive, to prevent possible loss of focus. Moreover, additional feedbacks have been devised to help external observers – therapists in the rehabilitation contexts or task planners and ergonomists in the industrial field – in the design and refinement of effective personalized tasks and long-term goals. The integration of all the hardware and software tools of each level in a modular, flexible, and reliable software framework, based on a well known robotic middleware, has been fundamental to handle the communication and information exchange processes. The developed general framework has been finally specialized to face the specific needs of robotic-aided gait rehabilitation. In this context, indeed, the final aim of promoting the symbiotic cooperation is translatable in maximizing treatment effectiveness for the patients by actively supporting their changing needs and capabilities while keeping them engaged during the whole rehabilitation process. The proposed multilevel framework specialization has been successfully used, as valuable answer to those needs, within the context of the Biomot European project. It has been, indeed, fundamental to face the challenges of closing the informative loop between the user and the device, and providing valuable quantitative information to the external observers. Within this research project, we developed an innovative compliant wearable exoskeleton prototype for gait rehabilitation capable of adjusting, at run-time, the provided support according to different cooperation strategies and to user needs and capabilities. At the same time, the wearer is also engaged in the rehabilitation process by intuitive visual feedbacks about his performances in the achievement of the rehabilitation targets and about the exoskeleton support. Both researchers and clinical experts evaluating the final rehabilitation application of the multilevel framework provided enthusiastic feedbacks about the proposed solutions and the obtained results. To conclude, the modular and generic multilevel framework developed in this thesis has the potential to push forward the current state of the art in the applications where a symbiotic cooperation between robotic devices and humans is required. Indeed, it effectively endorses the development of a new generation of robotic devices capable to perform challenging cooperative tasks in highly unpredictable environments while complying with the current needs, intentions, and capabilities of the human

A Computational Approach for the Design of Epidural Electrical Spinal Cord Stimulation Strategies to Enable Locomotion after Spinal Cord Injury

Spinal cord injury (SCI) is a major cause of paralysis with currently no effective treatment. Epidural electrical stimulation (EES) of the lumbar spinal cord has been shown to restore locomotion in animal models of SCI, but has not yet reached the same level of efficacy in humans. The mechanisms through which EES promotes locomotion, and the causes underlying these inter-species differences remain largely unknown, although essential to fully exploit the therapeutic potential of this neuromodulation strategy. Here, we addressed these questions using a deductive approach based on computer simulations and hypothesis-driven experiments, and proposed complementary strategies to enhance the current efficacy of EES-based therapies. In the first part of this thesis, we studied the mechanisms through which EES enables locomotion in rat models of SCI. Performing simulations and behavioral experiments, we provided evidence that EES modulates proprioceptive afferents activity, without interfering with the ongoing sensory signals. We showed that this synergistic interaction allows muscle spindle feedback circuits to steer the unspecific excitation delivered by EES to functionally relevant pathways, thus allowing the formation of locomotor patterns. By leveraging this understanding, we developed a stimulation strategy that allowed adjusting lesion-specific gait deficits, hence increasing the therapeutic efficacy of EES. In the second part of this thesis, we evaluated the influence of trunk posture on proprioceptive feedback circuits during locomotion, and thus on the effect of EES, in rat models of SCI. By combining modeling and experiments, we showed that trunk orientation regulates leg proprioceptive signals, as well as the motor patterns produced during EES-induced stepping. We exploited these results to develop a control policy that by automatically regulating trunk orientation significantly enhanced locomotor performance. In the last part of this thesis, we investigated the causes underlying species-specific effects of EES. Hypothesis-driven simulations suggested that in humans continuous EES blocks the proprioceptive signals traveling along the recruited fibers. We corroborated this prediction by performing experiments in rats and people with SCI. In particular, we showed that EES disrupts the conscious perception of leg movements, as well as the afferent modulation of sensorimotor circuits in humans, but not in rats. We provide evidence that in humans, due to this phenomenon, continuous EES can only facilitate locomotion to a limited extent. This was insufficient to provide clinically relevant improvements in the tested participants. Finally, we proposed two sensory-compliant stimulation strategies that might overcome these limitations, and thus augment the therapeutic efficacy of EES. In this thesis we elucidated key mechanisms through which EES promotes locomotion, we exposed critical limitations of continuous EES strategies when applied to humans, and we introduced complementary strategies to maximize the efficacy of EES therapies. These findings have far-reaching implications in the development of future strategies and technologies supporting the recovery of locomotion in people with SCI using EES