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

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.

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

Martina Coscia, Christian Giang, Nawal Noëlle Kinany, Silvestro Micera, Jenifer Cléa Miehlbradt, Camilla Pierella, Elvira Pirondini

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.

2020

MR-compatible robotics to investigate human motor control

Roger Gassert

Robotic interfaces can dynamically interact with humans performing movements and can be used to study neuromuscular adaptation. Such devices can produce computer controlled dynamics during movement and measure the interaction force and movement trajectory. On the other hand, functional magnetic resonance imaging (fMRI) can provide insight into the functional organization and plasticity of the brain. Using a robotic interface in conjunction with fMRI could allow neuroscientists to investigate the brain mechanisms of manipulation and motor learning, give therapists a tool for adaptive and patient-specific rehabilitation therapies, and assist medical doctors in functional diagnostics of motor dysfunctions. However, the MR environment imposes severe safety and electromagnetic compatibility constraints on mechatronic components, and the accessible workspace around the subject is limited. In addition, interaction with human motion must be safe and gentle. This thesis investigates the MR compatibility and performances of mechatronic elements, and develops an fMRI-compatible robotic technology consisting of sensors and actuators as well as adequate safety, control and synchronization strategies. It thus becomes possible to design various robotic systems for interaction with human motion during fMRI. This novel technology is benchmarked through the realization of several MR-compatible robotic systems which are currently being used by neuroscience groups in Japan and Europe. These include a highly MR-compatible interface for wrist motion, a tactile finger stimulation device using both intrinsically compatible and electrically powered components, as well as a two-degrees-of-freedom interface to investigate the control of multi-joint arm movements. The MR compatibility of these devices is successfully tested using a protocol developed for the particularly sensitive functional MRI sequences. In a further step towards performing multi-joint arm movements during fMRI, movement-related artifacts and biomechanical constraints are examined, and movements suitable for motor control studies are identified. Preliminary experiments with the realized systems demonstrate the potential of such robotic interfaces: the brain activation patterns during mental simulation of wrist motion are different from the patterns obtained during actual movements in interaction with the interface, and activation patterns in all conditions agree with results from literature. The well-controlled conditions as well as the recorded position and force data during fMRI open up new possibilities in data analysis and may allow new insights into the brain mechanisms involved in human motor control.

EPFL2006

Enactive robot vision

Mototaka Suzuki

The complexity of today's autonomous robots poses a major challenge for Artificial Intelligence. These robots are equipped with sophisticated sensors and mechanical abilities that allow them to enter our homes and interact with humans. For example, today's robots are almost all equipped with vision and several of them can move over rough terrain with wheels or legs. The methods developed so far in Artificial Intelligence, however, are not yet ready to cope with the complexity of the information gathered through the robot sensors and the need for rapid action in partially unknown and dynamic environments. In this thesis, I will argue that the apparent complexity of the environment and of the robot brain can be significantly simplified if perception, behavior, and learning are allowed to co-develop on the same time scale. In doing so, robots become sensitive to, and actively exploit, characteristics of the environment that they can tackle within their own computational and physical constraints. This line of work is grounded on philosophical and psychological research showing that perception is an active process mediated by behavior. However, computational models of active vision are very rare and often rely on architectures that are preprogrammed to detect certain characteristics of the environment. Previous work have shown that complex visual tasks, such as position and size invariant shape recognition as well as navigation, can be tackled with remarkably simple neural architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons were evolved while freely interacting with their environments. I proceed further on this line of investigation and describe the application of this methodology in three situations, namely car driving with an omnidirectional camera, goal-oriented navigation of a humanoid robot, and cooperative tasks by two agents. I will show that these systems develop sensitivity to a number of oriented, retinotopic, visual features – oriented edges, corners, height – and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system that allow them to accomplish their goals. In a second set of experiments, I will show that active vision can be exploited by the robot to perform anticipatory exploration of the environment in a task that requires landmark-based navigation. Evolved robots exploit an internal expectation system that makes use of active exploration to check for expected events in the environment. I will describe a third set of experiments where, in addition to an evolutionary process, the visual system of the robot can develop receptive fields by means of unsupervised Hebbian learning and show that these receptive fields are significantly affected by the behavior of the system and differ from those predicted by most computational models of visual cortex. Finally, I will show that these robots replicate the performance deficiencies observed in experiments of motor deprivation with kitten when they are exposed to the same type of motor deprivations. Furthermore, the analyses of our robot brains suggest an explanation for the deficiencies observed in kitten that have not yet been fully understood.

EPFL2007