Investigation of Virtual Reality as a new model of delivery for evidence-based stroke rehabilitation

Virtual reality-aided exercise-based training has shown promise for post-stroke upper limb motor recovery in the home. Robust studies are needed to develop evidence-based guidelines and facilitate uptake in clinical practice. Thus, a three-phase mixed methods design was used to (I) identify if VR can drive neural recovery; (II) incorporate end-users into the refinement of a device and (III) provide a robust feasibility trial within the home to inform a future clinical efficacy trial. Phase I was a systematic review that demonstrated there is insufficient robust data to identify neurophysiological changes correlated with or accompanying a reduction in motor impairment, in response to VR. The four included studies reported a varying impact of VR on motor recovery and were of poor quality. Thus, revealing the need for research to address the mechanisms by which VR potentially drives motor recovery, and for more robust initial investigations to guide the development of clinical trials. Phase II incorporated the views of ten stroke survivors, seven informal carers and nine clinicians into the refinement of a virtual reality device. Demonstrations of the Virtualrehab platform and a small home-trial confirmed the need for a low-cost non-immersive VR device that can deliver personalised home-based therapy. The end-users provided key recommendations for the next iteration of the device; in order to facilitate acceptability, usability and uptake of such technology. Phase III investigated the feasibility of delivering upper limb therapy via VR, within the home of eleven stroke survivors. The 12-week intervention demonstrated that this mode of delivery was feasible and acceptable to stroke survivors; of note was the 87.5% therapy adherence. The results identified practical challenges for delivering and investigating VR within the home; particularly recommendations for collecting neural and behavioural outcomes. Thus, providing results to inform a future dose-optimisation study and then a clinical efficacy trial

SVILUPPO DI DISPOSITIVI APTICI E USO DI REALTÀ VIRTUALE PER LA RIABILITAZIONE DELLA MANO E DELLE DITA

In the past decade, there has been an increasing attention for the development of personal and rehabilitation robots to assist, enhance, and quantify the rehabilitation therapy. This interest is expected to continue due to the improvements in health care that will allow people to live longer. A promising approach is the use of virtual reality in combination with haptic devices, i.e. manipulators that are capable of providing realistic force stimuli and accurate measurements of user’s movements, to treat the disability induced by stroke or chronic musculo-skeletal pathologies. The use of this technology not only helps to recover lost motor skills, but allows to obtain objective information on the rehabilitation process. In this research we have investigated the application of this approach in hand/finger rehabilitation of stroke patients. The first objective has been the development of a software framework that could support the flexibility and adaptability required by the addressed applications. Indeed, rehabilitation exercises have to be adapted to the patient disability, different devices must be integrated depending on the target of the rehabilitation, and different quantitative inforamtion should be recorded according to the objective of the trainign session. Based on the available tools, a software framework was developed using the Model/View/Controller software pattern that allows to decouple the different modules composing the application. Three main components were developed, namely: device management, virtual environment state evolution, and user interface. To reduce the cost, the framework was implemented only through the use of freely available libraries. The software framework was then used to develop a prototype application based on a five-bar linkage haptic device. During its development we paid special attention to the medial device regulations. Two main areas were identified as the most critical ones: the mechanical and physiological safety. A mechanical protection barrier for the hand/finger device was developed to ensure safe use by the user. At the same time, we investigated how to pursue physiological safey, i.e. how to monitor patients fatigue through the analysis of their physiological signals. When the patient enters a state of fatigue, it is necessary to change the intensity of the exercise according to the rehabilitation needs of the patient. The recognition of such state is a further element to be monitored during the control loop of the device, and that should be integrated in the analysis in real time. A first attempt to recognize the satte of fatigue was the observation of changes in the frequency band of electromyographic signals (EMG) of the patient. A framework for the acquisition of electromyographic signals, interfacing the rehabilitation system with an EMG amplifier, was developed. In the last part of the research, we investigated a different strategy for acquiring physiological signals of patient fatigue using the analysis of electroencephalographic signals (EEG). From an in-depth analysis of related literature, a software framework to integrate the analysis of EEG signals within the rehabilitation device conrol was identified. Additionally, a set of indexes for the definition of the level of mental fatigue of the patient was also designed

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

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

Movement-Related Desynchronization in EEG-based Brain-Computer Interface applications for stroke motor rehabilitation

Neurological degenerative diseases like stroke, Alzheimer, Amyothrophic Lateral Sclerosis (ALS), Parkinson and many others are constantly increasing their incidence in the world health statistics as far as the mean age of the global population is getting higher and higher. This leads to a general need for effective, at-home and low-cost rehabilitative and health-daily-care tools. The latter should consist either of technological devices implemented for operating in a remote way, i.e. tele-medicine is quickly spreading around the world, or very-advanced computer-based and robotic systems to realize intense and repetitive trainings. This is the challenge in which Information and Communications Technology (ICT) is asked to play a major role in order to bring medicine to reach further advancements. Indeed, no way to cope with these issues is possible outside a strong and vivid cooperation among multi-disciplinary teams of clinicians, physicians, biologists, neuro-psychologists and engineers and without a resolute pushing towards a widespread inter-operability between Institutes, Hospitals and Universities all over the world, as recently highlighted during the main International conferences on ICT in healthcare. The establishment of well-defined standards for gathering and sharing data will then represent a key element to enhance the efficacy of the aforementioned collaborations. Among the others, stroke is one of the most common neurological pathologies being the second or third cause of mortality in the world; moreover, it causes more than sixty percent survivors remain with severe cognitive and motor impairments that impede them in living normal lives and require a twenty-four-hours daily care. As a consequence, on one side stroke survivors experience a frustrating condition of being completely dependent on other people even to perform simple daily actions like reach and grasp an object, hold a glass of water to drink it and so on. States, by their side, have to take into account additional costs to provide stroke patients and their families with appropriate cares and supports to cope with their needs. For this reason, more and more fundings are recently made available by means of grants, European and International projects, programs to exchange different expertise among various countries with the aim to study how to accelerate and make more effective the recovery process of chronic stroke patients. The global research about this topic is conducted on several parallel aspects: as regard as the basic knowledge of brain processes, neurophysiologists, biologists and engineers are particularly interested in an in-depth understanding of the so-called neuroplastic changes that brain daily operates in order to adapt individuals to life changes, experiences and to realize more extensively their own potentialities. Neuroplasticity is indeed the corner stone for most of the trainings nowadays adopted by the standard as well as the more innovative methods in the rehabilitative programs for post-stroke recovery. Specifically speaking, motor rehabilitation usually includes long term, repetitive and intense goal-directed exercises that promote neuroplastic mechanisms such as neural sprouting, synapto-genesis and dendritic branching. These processes are strictly related with motor improvements and their study could - one day - serve as prognostic measures of the recovery. Another aspect of this eld of neuroscience research is the number of applications that it makes feasible. One of the most exciting is to connect an injured brain to a computer or a robotic device in a Brain-Computer or Brain-Machine Interface (BCI or BMI) scheme aiming at bypassing the impairments of the patient and make him/her autonomously move again or train his/her motor abilities in a more effective way. This kind of research can already count an amount of literature that provides several proofs of concept that these heterogeneous systems constituted by humans and robots can work at the purpose. A particular application of BCI for restoring or enhancing, at least, the reaching abilities of chronic stroke survivors was implemented and is still currently being improved at I.R.C.C.S. San Camillo Hospital Foundation, an Institute for the rehabilitation from neurological diseases located in Lido of Venice and partially technically supported by the Department of Information Engineering of Padua in range of an agreement signed in 2009. This specific BCI platform allows patients to train and improve their reaching movements by means of a robotic arm that provides a force that helps patients in completing the training exercise, i.e. to hit a predetermined target. This force feedback is however subject to a strict condition: during the movement, the person has to produce the expected pattern of cerebral activity. Whenever this is accomplished, a force is delivered proportionally to the entity of the latter activity, otherwise the patient is obliged to operate without any help. In this way, this platform implements the so-called operant-learning, that is one of the most effective conditioning techniques to make a subject learn or re-learn a task. If, on one hand, the primary and explicit task is to improve a movement, on the other side the secondary but most important task is to deploy the perilesional part of the brain - still healthy - in becoming responsible for the control of the movement. It is a popular and widely-accepted opinion within the neuroscience community, indeed, that a healthy region of the sensorimotor area nearby the damaged one - which was previously in charge of performing the (reaching) movement - can optimally accomplish the impaired motor function substituting the original control area. Technically speaking, the main crucial feature that can ensure the effectiveness of the whole system is the precise and in real-time identification and quantification of the cerebral pattern associated with the movement, the worldwide named movement-related desynchronization (MRD). Starting from its original definition, passing through the most used techniques for its recognition, the thesis work presents a series of criticisms of the current signal processing method to detect the MRD and a complete analysis of the possible features that can better represent the movement condition and that can be more easily extracted during the on-line operations. Brain - it is well-known - learns by trials and errors and it needs a slightly-delayed (in the range of fraction of seconds) feedback of its performance to learn a task in the best way. This BCI application was born with the purpose to provide the above-mentioned feedback: however, this is only feasible if a computationally easy and contingent signal processing technique is available. This thesis work would like to cope with the lack of a well-planned real-time signal analysis in the current experimental protocol

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Development of a hybrid assist-as-need hand exoskeleton for stroke rehabilitation.

Stroke is one of the leading causes of disability globally and can significantly impair a patient’s ability to function on a daily basis. Through physical rehabilitative measures a patient may regain a level of functional independence. However, required therapy dosages are often not met. Rehabilitation is typically implemented through manual one-to-one assistance with a physiotherapist, which quickly becomes labour intensive and costly. Hybrid application of functional electrical stimulation (FES) and robotic support can access the physiological benefits of direct muscle activation while providing controlled and repeatable motion assistance. Furthermore, patient engagement can be heightened through the integration of a volitional intent measure, such as electromyography (EMG). Current hybrid hand-exoskeletons have demonstrated that a balanced hybrid support profile can alleviate FES intensity and motor torque requirements, whilst improving reference tracking errors. However, these support profiles remain fixed and patient fatigue is not addressed. The aim of this thesis was to develop a proof-of-concept assist-as-need hybrid exoskeleton for post-stroke hand rehabilitation, with fatigue monitoring to guide the balance of support modalities. The device required the development and integration of a constant current (CC) stimulator, stimulus-resistant EMG device, and hand-exoskeleton. The hand exoskeleton in this work was formed from a parametric Watt I linkage model that adapts to different finger sizes. Each linkage was optimised with respect to angular precision and compactness using Differential Evolution (DE). The exoskeleton’s output trajectory was shown to be sensitive to parameter variation, potentially caused by finger measurement error and shifts in coupler placement. However, in a set of cylindrical grasping trials it was observed that a range of movement strategies could be employed towards a successful grasp. As there are many possible trajectories that result in a successful grasp, it was deduced that the exoskeleton can still provide functional assistance despite its sensitivity to parameter variation. The CC stimulator developed in this work has a part cost of USD $145 and allows flexible adjustment of waveform parameters through an on-board micro-controller. The device is designed to output current up to ±30mA given a voltage compliance of ±50V. When applied across a 2kΩ load, the device exhibited a linear output transfer function, with a maximum ramp tracking error of 5 The stimulus-resistant EMG device builds on current designs by using a novel Schmitt trigger based artefact detection channel to adaptively blank stimulation artefacts without stimulator synchronisation. The design has a part cost of USD$150 and has been made open-source. The device demonstrated its ability to record EMG over its predominant energy spectrum during stimulation, through the stimulation electrodes or through separate electrodes. Pearson’s correlation coefficients greater than 0.84 were identified be- tween the normalised spectra of volitional EMG (vEMG) estimates during stimulation and of stimulation-free EMG recordings. This spectral similarity permits future research into applications such as spectral-based monitoring of fatigue and muscle coherence, posing an advantage over current same-electrode stimulation and recording systems, which can- not sample the lower end of the EMG spectrum due to elevated high-pass filter cut-off frequencies. The stimulus-resistant EMG device was used to investigate elicited EMG (eEMG)-based fatigue metrics during vEMG-controlled stimulation and hybrid support profiles. During intermittent vEMG-controlled stimulation, the eEMG peak-to-peak amplitude (PTP) index was the median frequency (MDF) had a negative correlation for all subjects with R > 0:62 during stimulation-induced wrist flexion and R > 0:55 during stimulation-induced finger flexion. During hybrid FES-robotic support trials, a 40% reduction in stimulus intensity resulted in an average 21% reduction in MDF gradient magnitudes. This reflects lower levels of fatigue during the hybrid support profile and indicates that the MDF gradient can provide useful information on the progression of muscle fatigue. A hybrid exoskeleton system was formed through the integration of the CC stimulator, stimulus-resistant EMG device, and the hand exoskeleton developed in this work. The system provided assist-as-need functional grasp assistance through stimulation and robotic components, governed by the user’s vEMG. The hybrid support profile demonstrated consistent motion assistance with lowered stimulation intensities, which in-turn lowered the subjects’ perceived levels of fatigue

Proceedings of the 10th international conference on disability, virtual reality and associated technologies (ICDVRAT 2014)

The proceedings of the conferenc