Special sessions

Abstract:Walking impairments, caused by neurological, orthopedic, muscular, and cardiovascular disorders occur frequently and may have important physical and social impacts on a patient’s quality of life.

For instance, 70% of the patients who survive a stroke are unable to walk independently during the first three to four weeks post stroke. Improvements in walking ability (changes in walking speed, independence of walking) can be provoked by recovery of function in the affected leg to premorbid levels or by learning and using compensatory strategies. One of the primary goals during rehabilitation programs for patients with walking disorders, is to maintain and restore gait abilities, which entails increasing walking capacity while improving static and dynamic balance skills.

In the last two decades, robotic devices are increasingly accepted among many researchers and clinicians and they are being used in rehabilitation programs of physical impairments in lower limbs. Robotic Assisted Gait Rehabilitation is an intensive, repetitive and task-oriented training, which is generally in accordance with the motor re-learning program and several papers have demonstrated the feasibility and clinical effectiveness in subjects. However, to further improve its efficacy, robot assisted gait robots should further encourage patients to actively participate by applying new features and strategies to challenge the patient.

In this special session we invited several studies with patients using different approaches to challenge a patient during robot supported gait training, in order to foster clinically informed discussion on the most promising approaches in this direction.

Abstract:

Lower-limb, powered robotic exoskeletons have emerged as novel robot-assisted interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by non-invasive brain-machine interfaces (BMIs) that engage the patient and may promote cortical plasticity. In this session, we will show current developments about BMIs for controlling lower-limb powered exoskeletons, and will provide challenges to foster the use of these systems in spinal cord injury (SCI) and stroke survivors.

Abstract:

In the last decade, we have witnessed a change in the paradigms of rehabilitation towards a conception based on the promotion of the neuroplastic capacity of the central nervous system to promote motor re-learning, through the realization of functional, immersive and intensive therapies, providing neurosensory feedback consistent with the task, to promote the neuroplastic phenomenon. In the midterm, robotic technology arose in order to convey therapies designed on the foundations of these neuroplastic principles. Wearable robotic exoskeletons, stationary robotic trainers, virtual and augmented reality, wearable and ubiquitous sensing, as well as advanced control and computational models amongst others, have been largely proposed in the literature.

In the meanwhile, clinically-oriented studies have attempted to understand the actual benefits and outcomes of such technologies. The overall results indicate that these technologies are still failing to become a sustainable and competitive product in the market due to their complexity, cost, and limited efficacy. A misconceived implementation of neuroplastic principles appears to be the main reason. While these systems are all conceived as a generic solution for all kinds of patients, customization of technologies and therapies to the specific patient –pathology, functionality, preferences, etc- appears to be critical.

Little is known about how to design and deliver customized, patient-specific technology and therapies. While there is a broad consensus that the end-user –either patient, caregiver or clinician- must be the center of the process, there is a lack of a theoretical and practical body of knowledge on customization principles and technologies.

This Special Session aims at providing an overview on personalization methods and technologies of Robotic and Neuroprosthetic technologies as well as therapies for walking rehabilitation and assistance. End-user experiences related to this topic are also welcome.

Abstract: 

Robotic devices are becoming increasingly relevant in rehabilitation and assistive scenarios, to augment, train, supplement, or even replace motor functions. These devices have the potential to help addressing several issues liked to therapy, daily life support or workload relief. Besides features related to objective performance (designers’ viewpoint) or to rehabilitation outcomes (clinicians’ viewpoint), also aspects connected to subjective perception (users’ viewpoint) should be considered in the design and assessment of robotic devices physically interacting with humans. This is even more crucial in the case of individuals with neurological diseases and physical limitations. Despite this important need, there is no evidence on the substantial and systematic involvement of users in the design and assessment of rehabilitation and assistive robotic technologies. Moreover, current scientific literature demonstrates minimal consensus on the metrics and methods to be used to analyze user experience. This special session aims at soliciting discussion on the development of methods, protocols and metrics, based on psychological evaluations and on human factors, to assess user experience during the use of rehabilitation and assistive robots. These methods are expected to be used for the evaluation of already available technologies, for the user-centred design of new systems under development, and for the user-driven adaptation of robotic physical assistance. The special session will gather together not only researchers active in the neurorehabilitation field, but also experts from other application domains. This is expected to accelerate the transfer of knowledge from research fields where methods for the assessment of user experience and the evaluation of human factors are more consolidated.

Abstract:
The consequences of brain and spinal cord are very clearly long term and often progressive. Ongoing treatment options to promote recovery and support independence remain insufficient, and many patients feel abandoned after they have left hospital. Concerted efforts to create an evidence base to
justify a revolution in how we promote recovery after stroke are required. For example, the Stroke Association estimates that a £10M investment in research relating to stroke recovery could save the UK economy £750M. In an ageing population, the socioeconomic burden of brain and spinal cord injury and the cost of neurorehabilitation are steadily increasing. For example, in the UK alone, there are more people living with the consequences of stroke than with dementia (1.2M vs 0.85M) with an estimated annual cost of £26billion.
Neurorehabilitation is a complex medical process which aims to aid recovery from a nervous system injury, and to minimize and/or compensate for any functional alterations resulting from it. One key challenge is the development of treatment approaches that promote recovery through behavioural restitution, whilst minimizing recovery that is largely compensatory in nature. In addition, it is of critical importance to develop novel, more efficient and economic treatment approaches that can raise to the challenge of a growing need for neurorehabilitation.
The aim of this special session is to discuss strategies to improve the efficacy of neurorehabilitation as well as to improve quality of life of patients and their active reinsertion into society. This session provides an overview of recent developments in behavioural therapies (intensive upper limb
neurorehabilitation), and technology (robotic stroke therapies, and brain and spinal stimulation approaches) that offer possible avenues to improve the effectiveness of neurorehabilitation and its sustainability.

Abstract:

The analysis of human motion has been addressed for several decades and has reached a significant level of maturity. However, the generation of human motion is a more recent research topic and still presents great challenges. The objective can be either to simulate a particular type of human motion, as locomotion, or to predict the motion under certain conditions of a generic or specific human. Applications are highly relevant, including anticipation of surgery outcome, orthotic/prosthetic design and customization, control of humanoid robots and virtual characters, simulation of car accidents or ballistic impacts, training of athletes, ergonomy, etc. The aim of this special session is to gather contributions that address the problem of human motion generation from different approaches. Robotics, biomechanics, multibody dynamics or computer graphics are some of the communities which are actively working on this topic.

Abstract:

In the recent years, we have witnessed the emergence of promising novel neural interfaces that can be used to improve or restore motor control. These neural interfaces can be used for stimulation, e.g., deep brain stimulation, epidural spinal cord stimulation and intramuscular stimulation, or to indirectly assist or restore motor function through neural-controlled bypass devices (brain machine interfaces and myoelectric prosthetics). This Special Session will present new advances in the field and promote a discussion on its potential applications.

Abstract:

The effectiveness and efficiency of rehabilitation treatments are key factors for optimising the patient’s functional recovery and restoring autonomy and quality of life. In recent years, great attention has been paid to the humanization of the rehabilitation process, where the patient must be placed at the centre of treatment. This patient-oriented approach has been prompted with the development of new technologies in rehabilitation, which have revolutionized the modalities of treatment and assessment. Personalised, adaptable, early, intensive, task-specific, and multisensory approaches have been advocated to be the key requirements for successful rehabilitation treatment. At the same time, a complete and accurate technology-based assessment is crucial for a fine tuning of the rehabilitation process. However, the clinical settings present barriers limiting the widespread use of new technologies for a patient-tailored rehabilitation.

This special session of ICNR 2020 aims to promote and exhibit cutting edge research in new approaches for technology-based treatment and assessment with the purpose of personalizing the rehabilitation process with a patient-centred approach. In particular, this special session is dedicated to understanding the potential benefits and barriers of introducing such procedures in clinical practice.

Abstract:

Joint stiffness is the ‘hidden’ property of human movement control, and crucial for control of posture and movement. Joint stiffness describes the relationship between joint displacement and restoring torque. Our continuously changing environment requires joint stiffness to be tightly controlled to enable successful movement and interaction. For example, ankle stiffness varies during the gait cycle and elbow stiffness varies while making a reaching movement. Inability to adequately tune joint stiffness during movement has been associated with disorders of posture and movement following e.g. stroke or in Parkinson’s Disease. Knowledge on joint stiffness is not only highly desired and vital in order to unravel the origin of movement disorders but also to develop active biomimetic prosthesis, provide intuitive exoskeletons, and allow (humanoid) robots to ecologically interact with humans. The big challenge is to measure joint stiffness during movement.

In this symposium, all speakers will present work in which ‘joint stiffness’ stands central. The speakers will present new devices to measure dynamic joint stiffness during movement and demonstrate how these can be used to assess joint stiffness during movement of both upper and lower limb joints. In addition, they will address new time-varying system identification techniques to capture joint stiffness during movement, state-of-the art neuromuscular models to study how the neuromuscular and neural system control joint stiffness, and the application of these results in design of active prosthesis and exoskeletons.

Abstract:

 Motor learning, the underlying process of neurorehabilitation, is a complex cognitive and motor process associated with practice or experience leading to relatively permanent changes in the capability of motor performance. Fitts (1967) proposed that motor learning results from the incremental integration of environmental perception (i.e. mostly through visual information) and sensorimotor information (i.e. through proprioception). In his theory, subjects progress from a cognitive phase (using trial and error to discover the underlying rules of the environment), through an associative phase (in which motor strategies are formed by linking environmental stimuli and action outputs) to an autonomous phase (where motor strategies are consolidated and fine-tuned in memory as internal models). Indeed, research has shown that subject-specific factors (e.g. initial skill level, mood, age, etc) and task characteristics (e.g. the number of degrees of freedom, type of movement) crucially modulate earlier cognitive processes (e.g. attention, working memory, engagement) and later formation of motor strategies during learning. Consequently, a balance between subject-specific skills and environmental demands is crucial to facilitate motor learning and prevent frustration or, on the contrary, boredom. Thus, characterizing and quantifying these cognitive processes and motor strategies at the neural level (using Brain-Machine Interfaces (BMI), such as EEG or ECoG) may be useful to adapt feedback during training (e.g. using Virtual Reality or robotic devices) and optimize motor learning. 

In this special session, speakers (from the Industry, the clinics, and the Academy) will present and discuss how BMIs are being used to adapt the feedback provided in conventional motor training paradigms based on neural markers. The potential clinical and economic benefits for the healthcare system and, most importantly, the quality of life of the patients will be also discussed. 

Abstract:The marvelous complexity of human motor control appears evident when we think about the wide variety of coordinated actions that we can perform with our body: from mastering a musical instrument, to perform complex dancing choreographies, to quickly adjusting body balance in response to the rapidly changing dynamic conditions while standing on a surfing board.This is not only due to the sophisticated physical structure of our body but also to our control capabilities. It is established that these control capabilities rely on the sensory signals originated due to the interactions with the external environment and to the perception of the self. These signals are used by unimpaired individuals to represent the actions in the central nervous system through internal models, which underlie the anticipatory control mechanisms in charge of motor coordination.When the feedback is missing, the internal models are no longer updated resulting in degradation of the model, and ultimately in a deteriorated coordination. The lack of feedback can be due to several reasons: from the loss of an anatomical part (e.g., the amputation of a hand), to a neurological damage (e.g., a stroke), to the physical separation between the user and the end-effector (e.g., in tele-manipulation or assistive robotics).Congruency of sensory feedback with the intentional motor action is crucial, as well as coherency of the artificial feedback with expected perception in normal conditions. Yet, sense of touch involves a variety of features:  contact transients, direction of the interacting force, softness, stick-slip conditions, edges.  Perception of these cues is the result of complex stimulation of different mechanoreceptors, combined with proprioception and voluntary motor actions. In artificial sensory feedback, the challenge is to replicate rich and complex stimulation by means of an artificial device, or to find the most convenient trade-off between rendering capabilities and relevance of specific haptic cues in the manipulation tasks or in locomotion.Artificial sensory feedback has the potential to (re-)establish an intimate connection between the user and the controlled “tool”, would it be a prosthesis, a remote robotic limb, or a limb with impaired sensation. In fact, not only the artificial feedback can help strengthening the control of an alien limb but could promote neuro-rehabilitation processes by providing the brain with rich sensory information and ultimately stimulating neural plasticity.This special session wants to gather all the new ideas for translating the artificial sensory feedback into the future of prosthetics, rehabilitation and telemanipulation practice, encompassing methodologies, innovative devices, and approaches based on neuroscientific theories to deepen our knowledge on the design of functional devices and effective neuro-rehabilitation therapies.

Abstract:

When humans wear robotic devices (e.g., exoskeleton, prostheses), it is critical to synchronize human’s intention and robots movements to improve the human’s performance. Numerous studies have been done to tackle this challenge through 1) utilizing human intention to derive robot control command, and 2) optimizing robot control parameters to match the users’ behaviour pattern. On one hand, advanced technologies (including high-density electromyography (HD-EMG) recording, motor unit decomposition, neuromechanical modelling, etc.) enabled human intention detection with high accuracy and resolution, allowing real-time control of wearable robots with high degrees of freedom. On the other hand, researchers focused on personalizing the control parameters of wearable robots for each individual person through optimization approaches, which adapts the robots behaviour to synchronize with human’s movement pattern.

In this session, the goal is to promote and showcase advanced researches that advance the human-machine interface through different directions, and provide an opportunity for researchers to discuss potential combination of advances in both human intention detection and robot control optimization.

Abstract:

Non-invasive stimulation techniques such as transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and peripheral electrical stimulation (PES) have gained importance in investigating several clinical conditions in humans. The aim of the proposed session is to gather experts from world-class research groups and to present the latest findings of these techniques in stroke, spinal cord injury, and tremor. Specifically, we will present different TMS, tDCS, and PES protocols or a combination of them used to modulate corticospinal networks with the goal to improve motor functions.  We think that this session would make an important contribution to the ICNR 2020 because it will represent an opportunity to address and discuss new ideas about how non-invasive stimulation can be used for neurorehabilitation.

Abstract:

Superficial electromyography is a common measurement technique that allows the non-invasive record of muscle activity. The analysis of sEMG signals has been widely used to study human motor control. From the initial bipolar electrodes to the appearance of modern high-density EMG systems, muscle activity has been used to decode a wide range of parameters involved on human motion. Recently, the study of muscle synergies has shown great potential in decomposing motion in basic patterns that can be implemented in robotic systems to emulate human inspired movements.

A variety of approaches have been proposed so far to compute and analyse muscle synergies. This situation makes difficult to stablish the common sense for further understanding of human motion control and its adaptation to bioinspired robot control systems. The development of a standard methodology for the treatment of sEMG signals before and after synergy extraction will be an important achievement toward the creation of an efficient and effective network to share data and results.

In this special session, we will discuss the steps for the creation of such community introducing the state of the art of muscle synergies data processing and starting a network to share software for their extraction and analysis.

Abstract:

With global aging, the number of people with age related disabilities and impairments has been increasing sharply placing large demands on the global healthcare systems and aging-related services. Neurologic injuries, such as stroke, spinal cord injuries, Parkinson’s disease and weaknesses of the skeletal muscles with the aging population, greatly limits the ability to achieve basic activities for daily living and functional independence.
Owing to the advances in sensors and actuator technologies, innovative robotic systems have been developed as solutions to resolve such social and economic burden. In this special session, some of the state of the art robotic technologies (wearable sensors, powered AFOs or knees, exoskeleton suits, and etc.) specifically developed for daily assistance and rehabilitation will be presented and discussed.

Abstract:

Over the last decade many studies have proposed and tested a variety of robot-assisted therapeutic interventions. A shared motivation behind these approaches is providing effective and low-cost functional recovery after a neurological impairment. However, the reported results are highly variable, indicating that, among other factors, there is a need for a more in-depth understanding and quantification of the neuromechanical processes involved in robot-mediated therapy. Motor performance can now be assessed in a variety of levels, from the kinematic and dynamic behavior of movement to the evaluation of neurophysiological biomarkers.
Recent breakthroughs in electromyography (EMG) signal processing techniques, such as the introduction of high-density EMG, have broken new ground in our understanding of motor control all the way down to the motor unit level. While there is still room for improvement, further development of these techniques could be useful in terms of increasing knowledge on human behavior and in developing more advanced human-inspired assistive technologies. The evaluation of performance of the robot-assisted rehabilitation is a critical tool to adapt the therapy to the particular conditions and needs of the patient. Time is ripe for the combination of computational and sensing technologies to lead to the identification of new neuromechanical biomarkers that will help develop safer and standard rehabilitation therapies, track their effectiveness and enable researchers to gain a deeper understanding of the robot-assisted rehabilitation process as a whole.
This Special Session will discuss the application of neuromechanical biomarkers for evaluation of motor function recovery during robot-assisted therapies and their use as a tool to readapt the motor rehabilitation therapy to increase its effectiveness.