Design and control of an MRI compatible series elastic actuator

Bidirectional compatibility requirements with Magnetic Resonance Imaging (MRI) have limited the adaptation of rehabilitation robots for use in MRI machines. In this paper, we present the design and control of a Bowden cable-actuated, MRI-compatible series elastic actuator (SEA) that aims to fulfil the bidirectional compatibility requirements to the maximum extend. The proposed device is built using nonconductive diamagnetic MRI compatible materials, fiber optic sensing units and a Bowden cable based actuation, such that imaging artifacts created under strong magnetic field required for neuro-imaging are minimized. In particular, utilization of Bowden-cable transmission enables the placement of the conventional non-MRI compatible control/signal processing units and electric actuators outside the MRI room. This approach not only helps avoid the MR interference caused by these parts and eliminates safety hazards within the MRI room, but also ensures that the performance of the device is not affected by the strong magnetic field, resulting in ideal bidirectional MRI compatibility. Use of a custom-built fiber optic encoder together with nonconductive leaf spring based elastic element enables torque outputs of the device to be measured and used for closed-loop torque control, rendering the system into a series elastic actuator. The proposed MRI compatible SEA is easily customizable and can be used as the building block of higher degrees of freedom MRI compatible robotic devices. Current prototype is validated to administer continuous torques up to 2 Nm with a torque control bandwidth of 1 Hz and a torque sensing resolution of 0.05 Nm

Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation

Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration

A brain-computer interface integrated with virtual reality and robotic exoskeletons for enhanced visual and kinaesthetic stimuli

Brain-computer interfaces (BCI) allow the direct control of robotic devices for neurorehabilitation and measure brain activity patterns following the user’s intent. In the past two decades, the use of non-invasive techniques such as electroencephalography and motor imagery in BCI has gained traction. However, many of the mechanisms that drive the proficiency of humans in eliciting discernible signals for BCI remains unestablished. The main objective of this thesis is to explore and assess what improvements can be made for an integrated BCI-robotic system for hand rehabilitation. Chapter 2 presents a systematic review of BCI-hand robot systems developed from 2010 to late 2019 in terms of their technical and clinical reports. Around 30 studies were identified as eligible for review and among these, 19 were still in their prototype or pre-clinical stages of development. A degree of inferiority was observed from these systems in providing the necessary visual and kinaesthetic stimuli during motor imagery BCI training. Chapter 3 discusses the theoretical background to arrive at a hypothesis that an enhanced visual and kinaesthetic stimulus, through a virtual reality (VR) game environment and a robotic hand exoskeleton, will improve motor imagery BCI performance in terms of online classification accuracy, class prediction probabilities, and electroencephalography signals. Chapters 4 and 5 focus on designing, developing, integrating, and testing a BCI-VR-robot prototype to address the research aims. Chapter 6 tests the hypothesis by performing a motor imagery BCI paradigm self-experiment with an enhanced visual and kinaesthetic stimulus against a control. A significant increase (p = 0.0422) in classification accuracies is reported among groups with enhanced visual stimulus through VR versus those without. Six out of eight sessions among the VR groups have a median of class probability values exceeding a pre-set threshold value of 0.6. Finally, the thesis concludes in Chapter 7 with a general discussion on how these findings could suggest the role of new and emerging technologies such as VR and robotics in advancing BCI-robotic systems and how the contributions of this work may help improve the usability and accessibility of such systems, not only in rehabilitation but also in skills learning and education

Haptics Rendering and Applications

There has been significant progress in haptic technologies but the incorporation of haptics into virtual environments is still in its infancy. A wide range of the new society's human activities including communication, education, art, entertainment, commerce and science would forever change if we learned how to capture, manipulate and reproduce haptic sensory stimuli that are nearly indistinguishable from reality. For the field to move forward, many commercial and technological barriers need to be overcome. By rendering how objects feel through haptic technology, we communicate information that might reflect a desire to speak a physically- based language that has never been explored before. Due to constant improvement in haptics technology and increasing levels of research into and development of haptics-related algorithms, protocols and devices, there is a belief that haptics technology has a promising future

Co-adaptive control strategies in assistive Brain-Machine Interfaces

A large number of people with severe motor disabilities cannot access any of the available control inputs of current assistive products, which typically rely on residual motor functions. These patients are therefore unable to fully benefit from existent assistive technologies, including communication interfaces and assistive robotics. In this context, electroencephalography-based Brain-Machine Interfaces (BMIs) offer a potential non-invasive solution to exploit a non-muscular channel for communication and control of assistive robotic devices, such as a wheelchair, a telepresence robot, or a neuroprosthesis. Still, non-invasive BMIs currently suffer from limitations, such as lack of precision, robustness and comfort, which prevent their practical implementation in assistive technologies. The goal of this PhD research is to produce scientific and technical developments to advance the state of the art of assistive interfaces and service robotics based on BMI paradigms. Two main research paths to the design of effective control strategies were considered in this project. The first one is the design of hybrid systems, based on the combination of the BMI together with gaze control, which is a long-lasting motor function in many paralyzed patients. Such approach allows to increase the degrees of freedom available for the control. The second approach consists in the inclusion of adaptive techniques into the BMI design. This allows to transform robotic tools and devices into active assistants able to co-evolve with the user, and learn new rules of behavior to solve tasks, rather than passively executing external commands. Following these strategies, the contributions of this work can be categorized based on the typology of mental signal exploited for the control. These include: 1) the use of active signals for the development and implementation of hybrid eyetracking and BMI control policies, for both communication and control of robotic systems; 2) the exploitation of passive mental processes to increase the adaptability of an autonomous controller to the user\u2019s intention and psychophysiological state, in a reinforcement learning framework; 3) the integration of brain active and passive control signals, to achieve adaptation within the BMI architecture at the level of feature extraction and classification

Development and evaluation of a haptic framework supporting telerehabilitation robotics and group interaction

Telerehabilitation robotics has grown remarkably in the past few years. It can provide intensive training to people with special needs remotely while facilitating therapists to observe the whole process. Telerehabilitation robotics is a promising solution supporting routine care which can help to transform face-to-face and one-on-one treatment sessions that require not only intensive human resource but are also restricted to some specialised care centres to treatments that are technology-based (less human involvement) and easy to access remotely from anywhere. However, there are some limitations such as network latency, jitter, and delay of the internet that can affect negatively user experience and quality of the treatment session. Moreover, the lack of social interaction since all treatments are performed over the internet can reduce motivation of the patients. As a result, these limitations are making it very difficult to deliver an efficient recovery plan. This thesis developed and evaluated a new framework designed to facilitate telerehabilitation robotics. The framework integrates multiple cutting-edge technologies to generate playful activities that involve group interaction with binaural audio, visual, and haptic feedback with robot interaction in a variety of environments. The research questions asked were: 1) Can activity mediated by technology motivate and influence the behaviour of users, so that they engage in the activity and sustain a good level of motivation? 2) Will working as a group enhance users’ motivation and interaction? 3) Can we transfer real life activity involving group interaction to virtual domain and deliver it reliably via the internet? There were three goals in this work: first was to compare people’s behaviours and motivations while doing the task in a group and on their own; second was to determine whether group interaction in virtual and reala environments was different from each other in terms of performance, engagement and strategy to complete the task; finally was to test out the effectiveness of the framework based on the benchmarks generated from socially assistive robotics literature. Three studies have been conducted to achieve the first goal, two with healthy participants and one with seven autistic children. The first study observed how people react in a challenging group task while the other two studies compared group and individual interactions. The results obtained from these studies showed that the group interactions were more enjoyable than individual interactions and most likely had more positive effects in terms of user behaviours. This suggests that the group interaction approach has the potential to motivate individuals to make more movements and be more active and could be applied in the future for more serious therapy. Another study has been conducted to measure group interaction’s performance in virtual and real environments and pointed out which aspect influences users’ strategy for dealing with the task. The results from this study helped to form a better understanding to predict a user’s behaviour in a collaborative task. A simulation has been run to compare the results generated from the predictor and the real data. It has shown that, with an appropriate training method, the predictor can perform very well. This thesis has demonstrated the feasibility of group interaction via the internet using robotic technology which could be beneficial for people who require social interaction (e.g. stroke patients and autistic children) in their treatments without regular visits to the clinical centres

Implications of Action-Oriented Paradigm Shifts in Cognitive Science

An action-oriented perspective changes the role of an individual from a passive observer to an actively engaged agent interacting in a closed loop with the world as well as with others. Cognition exists to serve action within a landscape that contains both. This chapter surveys this landscape and addresses the status of the pragmatic turn. Its potential influence on science and the study of cognition are considered (including perception, social cognition, social interaction, sensorimotor entrainment, and language acquisition) and its impact on how neuroscience is studied is also investigated (with the notion that brains do not passively build models, but instead support the guidance of action). A review of its implications in robotics and engineering includes a discussion of the application of enactive control principles to couple action and perception in robotics as well as the conceptualization of system design in a more holistic, less modular manner. Practical applications that can impact the human condition are reviewed (e.g., educational applications, treatment possibilities for developmental and psychopathological disorders, the development of neural prostheses). All of this foreshadows the potential societal implications of the pragmatic turn. The chapter concludes that an action-oriented approach emphasizes a continuum of interaction between technical aspects of cognitive systems and robotics, biology, psychology, the social sciences, and the humanities, where the individual is part of a grounded cultural system

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

Enhancing brain/neural-machine interfaces for upper limb motor restoration in chronic stroke and cervical spinal cord injury

Operation of assistive exoskeletons based on voluntary control of sensorimotor rhythms (SMR, 8-12 Hz) enables intuitive control of finger or arm movements in severe paralysis after chronic stroke or cervical spinal cord injury (SCI). To improve reliability of such systems outside the laboratory, in particular when brain activity is recorded non-invasively with scalp electroencephalography (EEG), a hybrid EEG/electrooculography (EOG) brain/neural-machine interface (B/NMI) was recently introduced. Besides providing assistance, recent studies indicate that repeated use of such systems can trigger neural recovery. However, important prerequisites have to achieved before broader use in clinical settings or everyday life environments is feasible. Current B/NMI systems predominantly restore hand function, but do not allow simultaneous control of more proximal joints for whole-arm motor coordination as required for most stroke survivors suffering from paralysis in the entire upper limb. Besides paralysis, cognitive impairments including post-stroke fatigue due to the brain lesion reduce the capacity to maintain effortful B/NMI control over a longer period of time. This impedes the applicability in daily life assistance and might even limits the efficacy of neurorehabilitation training. In contrast to stroke survivors, tetraplegics due to cervical SCI lack motor function in both hands. Given that most activities of daily living (ADL) involve bimanual manipulation, e.g., to open the lid of a bottle, bilateral exoskeleton control is required but was not shown yet in tetraplegics. To further enhance B/NMI systems, we first investigated whether B/NMI whole-arm exoskeleton control in hemiplegia after chronic stroke is feasible and safe. In contrast to simple grasping, control of more complex tasks involving the entire upper limb was not feasible with established B/NMIs because high- dimensionality of such multiple joint systems exceeds the bandwidth of these interfaces. Thus, we blended B/NMI control with vision-guidance to receive a semiautonomous whole-arm exoskeleton control. Such setup allowed to divide ADL tasks into a sequence of EEG/EOG-triggered sub-tasks reducing complexity for the user. While, for instance, a drinking task was resolved into EOG-induced reaching, lifting and placing back the cup, grasping and releasing movements were based on intuitive SMR control. Feasibility of such shared vision-guided B/NMI control was assumed when executions were initialized within 3 s (fluent control) and a minimum of 75 % of subtasks were executed within that time (reliable control). We showed feasibility in healthy subjects as well as stroke survivors without report of any side effects documenting safe use. Similarly, feasibility and safety of bilateral B/NMI control after cervical SCI was evaluated. To enable bilateral B/NMI control, established EEG-based grasping and EOG-based releasing or stop commands were complemented with a novel EOG command allowing to switch laterality by performing prolonged horizontal eye movements (>1 s) to the left or to the right. Study results with healthy subjects and tetraplegics document fluent initialization of grasping motions below 3 s as well as safe use as unintended grasping could be stopped before a full motion was conducted. Superiority of novel bilateral control was documented by a higher accuracy of up to 22 % in tetraplegics compared to a bilateral control without prolonged EOG command. Lastly, as reliable B/NMI control is cognitively demanding, e.g., by imagining or attempting the desired movements, we investigated whether heart rate variability (HRV) can be used as biomarker to predict declining control performance, which is often reported in stroke survivors due to their cognitive impairments. Referring to the close brain-heart connection, we showed in healthy subjects that a decline in HRV is specific as well as predictive to a decline in B/NMI control performance within a single training session. The predictive link was revealed by a Granger-causality analysis. In conclusion, we could demonstrate important enhancements in B/NMI control paradigms including complex whole-arm exoskeleton control as well as individual performance monitoring within a training session based on HRV. Both achievements contribute to broaden the use as a standard therapy in stroke neurorehabilitation. Especially the predictive characteristic of HRV paves the way for adaptive B/NMI control paradigms to account for individual differences among impaired stroke survivors. Moreover, we also showed feasibility and safety of a novel implementation for bilateral B/NMI control, which is necessary for reliable operation of two hand-exoskeletons for bimanual ADLs after SCI

Development of Finger ContracturePrevention System For Early Post StrokeRehabilitation

芝浦尾工業大学2016年