Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

Electroencephalography (EEG)-based brain computer interfaces for rehabilitation

Objective: Brain-computer interface (BCI) technologies have been the subject of study for the past decades to help restore functions for people with severe motor disabilities and to improve their quality of life. BCI research can be generally categorized by control signals (invasive/non-invasive) or applications (e.g. neuroprosthetics/brain-actuated wheelchairs), and efforts have been devoted to better understand the characteristics and possible uses of brain signals. The purpose of this research is to explore the feasibility of a non-invasive BCI system with the combination of unique sensorimotor-rhythm (SMR) features. Specifically, a 2D virtual wheelchair control BCI is implemented to extend the application of previously designed 2D cursor control BCI, and the feasibility of the prototype is tested in electroencephalography (EEG) experiments; guidance on enhancing system performance is provided by a simulation incorporating intelligent control approaches under different EEG decoding accuracies; pattern recognition methods are explored to provide optimized classification results; and a hybrid BCI system is built to enhance the usability of the wheelchair BCI system. Methods: In the virtual wheelchair control study, a creative and user friendly control strategy was proposed, and a paradigm was designed in Matlab, providing a virtual environment for control experiments; five subjects performed physical/imagined left/right hand movements or non-control tasks to control the virtual wheelchair to move forward, turn left/right or stop; 2-step classification methods were employed and the performance was evaluated by hit rate and control time. Feature analysis and time-frequency analysis were conducted to examine the spatial, temporal and frequency properties of the utilized SMR features, i.e. event-related desynchronization (ERD) and post-movement event-related synchronization (ERS). The simulation incorporated intelligent control methods, and evaluated navigation and positioning performance with/without obstacles under different EEG decoding accuracies, to better guide optimization. Classification methods were explored considering different feature sets, tuned classifier parameters and the simulation results, and a recommendation was provided to the proposed system. In the steady state visual evoked potential (SSVEP) system for hybrid BCI study, a paradigm was designed, and an electric circuit system was built to provide visual stimulus, involving SSVEP as another type of signal being used to drive the EEG BCI system. Experiments were conducted and classification methods were explored to evaluate the system performance. Results: ERD was observed on both hemispheres during hand\u27s movement or motor imagery; ERS was observed on the contralateral hemisphere after movement or motor imagery stopped; five subjects participated in the continuous 2D virtual wheelchair control study and 4 of them hit the target with 100% hit rate in their best set with motor imagery. The simulation results indicated that the average hit rate with 10 obstacles can get above 95% for pass-door tests and above 70% for positioning tests, with EEG decoding accuracies of 70% for Non-Idle signals and 80% for idle signals. Classification methods showed that with properly tuned parameters, an average of about 70%-80% decoding accuracy for all the classifiers could be reached, which reached the requirements set by the simulation test. Initial test on the SSVEP BCI system exhibited high classification accuracy, which may extend the usability of the wheelchair system to a larger population when finally combined with ERD/ERS BCI system. Conclusion: This research investigated the feasibility of using both ERD and ERS associated with natural hand\u27s motor imagery, aiming to implement practical BCI systems for the end users in the rehabilitation stage. The simulation with intelligent controls provided guides and requirements for EEG decoding accuracies, based on which pattern recognition methods were explored; properly selected features and adjusted parameters enabled the classifiers to exhibit optimal performance, suitable for the proposed system. Finally, to enlarge the population for which the wheelchair BCI system could benefit for, a SSVEP system for hybrid BCI was designed and tested. These systems provide a non-invasive, practical approach for BCI users in controlling assistive devices such as a virtual wheelchair, in terms of ease of use, adequate speed, and sufficient control accuracy

Effectiveness of intensive physiotherapy for gait improvement in stroke: systematic review

Introduction: Stroke is one of the leading causes of functional disability worldwide. Approximately 80% of post-stroke subjects have motor changes. Improvement of gait pattern is one of the main objectives of physiotherapists intervention in these cases. The real challenge in the recovery of gait after stroke is to understand how the remaining neural networks can be modified, to be able to provide response strategies that compensate for the function of the affected structures. There is evidence that intensive training, including physiotherapy, positively influences neuroplasticity, improving mobility, pattern and gait velocity in post-stroke recovery. Objectives: Review and analyze in a systematic way the experimental studies (RCT) that evaluate the effects of Intensive Physiotherapy on gait improvement in poststroke subjects. Methodology: Were only included all RCT performed in humans, without any specific age, that had a clinical diagnosis of stroke at any stage of evolution, with sensorimotor deficits and functional gait changes. The databases used were: Pubmed, PEDro (Physiotherapy Evidence Database) and CENTRAL (Cochrane Center Register of Controlled Trials). Results: After the application of the criteria, there were 4 final studies that were included in the systematic review. 3 of the studies obtained a score of 8 on the PEDro scale and 1 obtained a score of 4. The fact that there is clinical and methodological heterogeneity in the studies evaluated, supports the realization of the current systematic narrative review, without meta-analysis. Discussion: Although the results obtained in the 4 studies are promising, it is important to note that the significant improvements that have been found, should be carefully considered since pilot studies with small samples, such as these, are not designed to test differences between groups, in terms of the effectiveness of the intervention applied. Conclusion: Intensive Physiotherapy seems to be safe and applicable in post-stroke subjects and there are indications that it is effective in improving gait, namely speed, travelled distance and spatiotemporal parameters. However, there is a need to develop more RCTs with larger samples and that evaluate the longterm resultsN/

New techniques for neuro-rehabilitation: Transcranial Electric Stimulation and Virtual Reality

Recovery of motor and cognitive performances after a neurological illness remains a significant challenge for rehabilitation specialists. The traditional rehabilitative interventions are usually delivered using a multidisciplinary approach, whose results are not always satisfactory. These limitations in functional recovery have led researchers to consider alternative approaches. The hypothesis of providing new therapeutic possibilities in the different patients treated is, as a rehabilitator, very rewarding and represents a challenge for the future. The application of simple and low-cost techniques, defined by the literature as "unconventional" or “novel”, can provide new ideas not only in the field of research but above all of application in clinical reality.A suitable approach to improve the rehabilitation outcome is to utilize these novel rehabilitation techniques that act as a substitute or an addition to the traditional ones. In this context, some recent approaches have been proposed that might increase the effectiveness of a traditional treatment. Among them, two techniques have been demonstrated to be very promising, namely non-invasive brain stimulation (NIBS) and Virtual Reality (VR).In light of the foregoing, my thesis has been divided into two main lines of research, namely: a) the study of the effects of transcranial direct current stimulation (tDCS) in different neurological conditions; b) the application of VR (used alone or combined with tDCS) in the treatment of some neurocognitive disorders. A semi-immersive VR tool (ReMOVES system) has been used as a user-friendly platform providing activities based on exergames

Разработка программного комплекса для реабилитации двигательных функций

Большое количество людей страдает от неврологических нарушений. Для реабилитации таких пациентов применяется совокупность различных методов, так как не существует одного универсального способа реабилитации. Одним из методов является применение дополненной реальности. В данном исследовании были изучены варианты ее применения для реабилитации пациентов с неврологическими нарушениями, изучено оборудование для реализации данного метода. По итогам была разработана система для проведения БОС-тренингов в условиях дополненной реальности, три сценария БОС-тренингов, а также программа для визуализации и математического анализа данных с Leap Motion. Применение дополненной реальности в совокупности с другими методами позволит улучшить общую эффективность реабилитации.A large number of people suffer from neurological disorders. Various methods are used for rehabilitation of such patients, because there is no single universal method of rehabilitation. One of the methods is the use of augmented reality. Variants of its use for the rehabilitation of patients with neurological disorders were studied, and the equipment for implementing this method was studied in this research work. As a result, a system for bio-feedback training using augmented reality was developed; three bio-feedback training scenarios were created, as well as a program for visualization and mathematical analysis of data from the Leap Motion. The use of augmented reality in conjunction with other methods will improve the overall effectiveness of rehabilitation

Efficacy of Rhythmic Acquisition on Gait Performance Among Individuals with Parkinson’s Disease

The purpose of this study was to identify the ability of individuals with Parkinson’s disease (PD) to acquire different rhythmic complexity levels through individual home-based Improvised Active Music Therapy (IAMT) sessions. The study aimed to identify whether higher acquisition of rhythmic complexity levels improved gait performance, as well as beat perception and production abilities. In this single subject multiple baseline design, the study measured the ability of four right-handed participants with PD to acquire greater density of syncopation, as a measure of rhythmic complexity levels, while playing uninterrupted improvised music on a simplified electronic drum-set. An accredited music therapist led each session with an acoustic guitar. The study described how higher density of syncopation levels presented in participants’ playing related to not only gait performance, and beat perception and production abilities, but also to other music measurements. The participants’ music content was transformed into digital music data in real time using Musical Instrument Digital Interface (MIDI). MIDI data was analyzed to determine density of syncopation, note count, velocity, and asynchrony during baseline and treatment IAMT intervention. Results from visual analyses and Pearson correlations indicated partial evidence for the ability of individuals with PD to acquire different rhythmic complexity levels through IAMT. Partial evidence was also found to support the overall effectiveness of IAMT sessions in increasing participant’s mean gait velocity and stride length, and reducing step time and stride length variability. The findings of the current study indicate that IAMT sessions could be an effective strategy to increase physical mobility among individuals with PD. Using MIDI in the IAMT approach can yield data to evaluate treatment effectiveness and assess patient progress, providing daily measures and analysis of data using statistical analyses alongside visual analysis. This method has the potential to lead to new evidence-based interventions modeled in music therapy