Robotic neurorehabilitation: a computational motor learning perspective

Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols

I-BaR: Integrated Balance Rehabilitation Framework

Neurological diseases are observed in approximately one billion people worldwide. A further increase is foreseen at the global level as a result of population growth and aging. Individuals with neurological disorders often experience cognitive, motor, sensory, and lower extremity dysfunctions. Thus, the possibility of falling and balance problems arise due to the postural control deficiencies that occur as a result of the deterioration in the integration of multi-sensory information. We propose a novel rehabilitation framework, Integrated Balance Rehabilitation (I-BaR), to improve the effectiveness of the rehabilitation with objective assessment, individualized therapy, convenience with different disability levels and adoption of an assist-as-needed paradigm and, with an integrated rehabilitation process as a whole, i.e., ankle-foot preparation, balance, and stepping phases, respectively. Integrated Balance Rehabilitation allows patients to improve their balance ability by providing multi-modal feedback: visual via utilization of Virtual Reality; vestibular via anteroposterior and mediolateral perturbations with the robotic platform; proprioceptive via haptic feedback.Comment: 37 pages, 2 figures, journal pape

Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

Development Of Robot-Based Cognitive And Motor Assessment Tools For Stroke And Hiv Neurorehabilitation

Stroke and HIV are leading causes of disability worldwide. HIV is an independent risk factor for stroke, resulting in an emerging population dealing with both but without guidelines on how to manage the co-presentation of these conditions. There is a need for solutions to combat functional decline that results from the cognitive and motor dysfunction associated with these conditions. Rehabilitation robotics has been explored as a solution to provide therapy in the stroke population, but its application to people living with HIV has not yet been examined. Additionally, current technology-based approaches generally tend to treat cognitive and motor impairments in isolation. As such, a major barrier to the clinical utility of these approaches is that improvements on robotic rehabilitation tasks do not transfer to activities of daily living. In this thesis, I combine rehabilitation robotics, cognitive neuroscience, and bioengineering principles to design robot-based assessment tasks capable of measuring both cognitive and motor impairment. I use clinical assessment and robotic tools to first explore the impact of cognitive impairment on motor performance in the chronic stroke population. The results from this investigation demonstrate that motor performance on a robotic task is sensitive to cognitive impairment due to stroke. I then tested additional assessment tasks against standard clinical assessments of cognitive and motor function relevant in both HIV and stroke. These results showed the ability of robot-based metrics to capture differences in performance between varying levels of impairment among people living with HIV. After demonstrating the concurrent validity of this approach in the U.S., I implemented this approach in Botswana. The preliminary results demonstrated that robotic assessment was feasible in this context and that some of our models had good predictive value. This work expands the application of rehabilitation robotics to new populations, including people living with HIV, those with cognitive impairments, and people residing in LMICs. My hope is that the work presented in this thesis will lead to future efforts that can overcome the barriers to better health by enabling the development of more effective and accessible rehabilitation technologies

EEG and ECoG features for Brain Computer Interface in Stroke Rehabilitation

The ability of non-invasive Brain-Computer Interface (BCI) to control an exoskeleton was used for motor rehabilitation in stroke patients or as an assistive device for the paralyzed. However, there is still a need to create a more reliable BCI that could be used to control several degrees of Freedom (DoFs) that could improve rehabilitation results. Decoding different movements from the same limb, high accuracy and reliability are some of the main difficulties when using conventional EEG-based BCIs and the challenges we tackled in this thesis. In this PhD thesis, we investigated that the classification of several functional hand reaching movements from the same limb using EEG is possible with acceptable accuracy. Moreover, we investigated how the recalibration could affect the classification results. For this reason, we tested the recalibration in each multi-class decoding for within session, recalibrated between-sessions, and between sessions. It was shown the great influence of recalibrating the generated classifier with data from the current session to improve stability and reliability of the decoding. Moreover, we used a multiclass extension of the Filter Bank Common Spatial Patterns (FBCSP) to improve the decoding accuracy based on features and compared it to our previous study using CSP. Sensorimotor-rhythm-based BCI systems have been used within the same frequency ranges as a way to influence brain plasticity or controlling external devices. However, neural oscillations have shown to synchronize activity according to motor and cognitive functions. For this reason, the existence of cross-frequency interactions produces oscillations with different frequencies in neural networks. In this PhD, we investigated for the first time the existence of cross-frequency coupling during rest and movement using ECoG in chronic stroke patients. We found that there is an exaggerated phase-amplitude coupling between the phase of alpha frequency and the amplitude of gamma frequency, which can be used as feature or target for neurofeedback interventions using BCIs. This coupling has been also reported in another neurological disorder affecting motor function (Parkinson and dystonia) but, to date, it has not been investigated in stroke patients. This finding might change the future design of assistive or therapeuthic BCI systems for motor restoration in stroke patients