Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study

Background In the past years, robotic systems have become increasingly popular in upper limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients' individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches. Methods Here, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients' motor improvement for a series of point-to-point reaching movements in different directions. Additionally, it comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper-limb exoskeleton. The implementation was tested with 17 healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test undergoing rehabilitative motor training. Results The results of the exploratory study with healthy subjects showed that the participants divided into fast and slow adapters. The model was able to correctly estimate distinct motor improvement progressions between the two groups of participants while proposing individual training protocols. For the two pilot patients, an analysis of the selected motor performance measures showed that both patients were able to retain the improvements gained during training when reaching movements were reintroduced at a later stage. These results suggest that the automated training adaptation was appropriately timed and specifically tailored to the abilities of each individual. Conclusions The results of our exploratory study demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing encouraging results for the applicability in clinical settings.

A multimodal framework for the design and the assessment of robotic-assisted treatments for motor disorders

Elvira Pirondini

Robotic devices represent an undeniable alternative method to traditional therapy. However, the clinical use of robot-assisted rehabilitation for patients with hemiplegia after stroke has been restricted for decades by i) limited functional characterisation of robotic devices; ii) non-efficient and widely accepted robot-measured kinematic parameters to assess performances of robot-assisted therapies; and iii) limited identification of rehabilitative strategies based on recovery mechanisms. This led to a misuse of robotic systems and poorly effective clinical trials. For example, a recent work in a large cohort of patients showed that the use of an upper limb exoskeleton, compared to conventional therapy, promoted a larger arm mobility recovery but also a lower improvement in arm strength. These ambiguous results strengthen the idea that, while robotic devices provide a mean for intensive practice, hand-on would always be crucial for turning benefits into functional gains. I argue that the limits of robot-assisted rehabilitation stem from a lack of fundamental understanding of its interactions with the human sensori-motor system and not by an intrinsic limited value. To this aim I developed a multimodal framework to investigate and characterise the full spectrum of interactions between a robot and the human sensori-motor system, from muscles to brain signals. This framework could boost robot-assisted rehabilitation and, thus, its efficacy for daily clinical practice. This framework includes i) a new developed exoskeleton âi.e., Arm Light Exoskeleton (ALEx); ii) a computational model for the selection of the optimal robot-measured kinematic parameters; iii) a novel statistical tool for the analysis of the brain activity; iv) an advanced signal processing technique to analyse the brain activity in relation to muscle activity during arm movements. Overall, results show that i) a deep characterisation of robotic systems allows understanding the robot-human sensori-motor system interactions and selecting optimal assistive modalities for the rehabilitative training; ii) robot-measured kinematic parameters can be informative of central and/or peripheral mechanisms of motor recovery; iii) the analysis of the cortico-muscular organisation supported by advanced statistical methods could be useful to monitor the neuro-biomechanical state of post-stroke patients. These results might have important implications in the understanding of the neural correlates of motor recovery to drive successful robot-assisted rehabilitation. A clinical trial that applies the conceptual framework, scientific rationale, and technical details explained in this Thesis has recently started at the University Hospital of Geneva, Switzerland, and at the University Hospital of Pisa, Italy. The study aims, among other goals, at defining the neuro-biomechanical state of the patient and its evolution during the therapy by using the multimodal framework here presented.

EPFL2017

A Model-Based Control Approach for Locomotion of Biped Robots

Salman Faraji

In this research we aim at proposing a general novel walking method for locomotion of torque controlled robots. The method should be able to produce a wide range of speeds without requiring oﬀ-line optimizations and re-tuning of parameters. It should be capable of tolerating internal errors, noises and control delays as well as external disturbances such as pushes or roughness in the environment. We have a quadratic whole-body optimization which generates joint toques, given desired Cartesian accelerations of center of mass and feet. Using dynamics model of the robot inside this optimizer ensures compliance and better tracking, required for fast locomotion. We have simpliﬁed the model of robot to linear inverted pendulum and proposed diﬀerent planners which are other quadratic convex problems optimizing future behavior of the robot. These planners are in fact model predictive control which optimize the system either in continuous or discrete time domains. Fast libraries help us performing these calculations per time step and producing desired motion. With very few parameters to tune and no perception, our method shows notable robustness against strong external pushes, large terrain variations, internal noises, model errors and also delayed communication. Evident by various simulations in diﬀerent conditions, we can suggest our general method for walking control of a wide range of humanoid robots.

2013

Neuroprosthetic rehabilitation and translational mechanism after severe spinal cord injury

Lucia Florinda Friedli Wittler

Traumatic SCIs have long-term health, economic and social consequences, stressing the urgency to develop interventions to improve recovery after such injuries. Today, the only proven effective interventions to enhance recovery after SCI are activity-based rehabilitation therapies, such as locomotor training. However, locomotor training shows no or very limited efficacy to improve function after a severe SCI that induces paralysis of the limbs. To mimic the outcome of severe but incomplete SCI in rodents, we developed a model of double opposite-side lateral hemisections termed staggered hemisection in adult rats. This model induced permanent paralysis below the level of injury but leaves an intervening gap of intact neural tissue that provides a substrate for recovery. We showed that this SCI leads to degradation of motor functions, which correlates with the formation of aberrant neuronal connections below the lesion. Robotic devices with a rehabilitative purpose should act as propulsive or postural neuroprosthesis allowing training under natural conditions. Our versatile robotic interface provides multidirectional bodyweight support during overground locomotion in rats. We next evaluated the effects of robot-assisted gait training enabled by electrochemical stimulation of spinal circuits to restore locomotion after staggered hemisection SCI. We found that after two months of daily training, paralyzed rats recovered the ability to initiate, sustain and adjust bipedal locomotion while supported in the robot under electrochemical stimulation. This recovery correlated with ubiquitous reorganization of corticospinal, brainstem, and intraspinal fibers. We next evaluated whether this treatment was capable of restoring supraspinal control of locomotion after a clinically relevant SCI. Rats received a severe contusion of the spinal cord that spared less than 10% of intact tissue. Robot-assisted rehabilitation restored weight-bearing locomotion in all the trained rats when stimulated electrochemicallay and in a subset of rats in the absence of any enabling factors which paralelled with the reorganization of axonal projections of reticulospinal fibers below the contusion. Virus-mediated silencing of reticulospinal neurons projecting to lumbar segments demonstrated that these inputs were necessary to initiate and sustain walking after training. When delaying the onset of training by two months, in the chronic stage, all the rats regained voluntary locomotor movements but the extent of the recovery was reduced compared to rats trained early after SCI. The results provide a strong rationale to evaluate the impact of neuroprosthetic training to improve motor functions in human patients with incomplete SCI. Translation of treatment paradigms developed in rodent models into effective clinical applications remains a major challenge in biomedical research. Here, we studied recovery of motor functions in more than 400 quadriplegic patients who presented various degree of spinal cord damage laterality. We found that recovery increases with the asymmetry of early motor deficits. We conclude that emergence of spinal cord decussating corticospinal fibers and bilateral motor cortex projections during mammalian evolution supports greater recovery after lateralized SCI primates compared to rodents. Novel experimental models and dedicated therapeutic strategies are necessary to take advantage of this powerful neuronal substrate for recovery after SCI.