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

Diseño de entornos de realidad virtual aplicables a sistemas de robótica asistencial: un análisis literario

Virtual Reality (VR) environments can be applied to assistive robotics to improve the effectiveness and the user experience perception in the rehabilitation process due to its innovative nature, getting to entertain patients while they recover their motor functions. This literature review pretends to analyze some design principles of VR environments developed for upper limb rehabilitation processes. The idea is to identify features related to peripheral and central nervous systems, types of information included as feedback to increase the user's levels of immersion having a positive impact on the user's performance and experience during the treatment. A total of 32 articles published in Scopus, IEEE, PubMed, and Web of Science in the last four years were reviewed. We present the article selection process, the division by concepts presented previously, and the guidelines that can be considered for the design of VR environments applicable to assistive robots for upper limbs rehabilitation processes.Los entornos de Realidad Virtual (RV) aplicables a sistemas de robótica asistencial pueden ser diseñados de manera que mejoren la efectividad y la experiencia de usuario de los procesos de rehabilitación debido a su naturaleza novedosa, logrando entretener a los pacientes mientras recuperan sus funciones motoras. Esta revisión literaria pretende analizar los criterios de diseño de entornos de RV utilizados en procesos de rehabilitación de miembro superior, identificando las características de entornos para rehabilitación de problemas asociados el sistema nervioso central y periféricos, los tipos de información que se realimenta al usuario para beneficiar los niveles de inmersión y su impacto en términos del desempeño y la experiencia del usuario en tratamiento. Un total de 32 artículos publicados en revistas indexadas de Scopus, IEEE, PubMed y Web of Science en los últimos cuatro años fueron revisados. Se presenta el proceso de selección de artículos, la división por las temáticas presentadas anteriormente y los lineamientos generales que pueden ser considerados para el diseño de entornos de RV aplicables a robots asistenciales en procesos de rehabilitación de miembro superior

Motion capture sensing techniques used in human upper limb motion: a review

Purpose Motion capture system (MoCap) has been used in measuring the human body segments in several applications including film special effects, health care, outer-space and under-water navigation systems, sea-water exploration pursuits, human machine interaction and learning software to help teachers of sign language. The purpose of this paper is to help the researchers to select specific MoCap system for various applications and the development of new algorithms related to upper limb motion. Design/methodology/approach This paper provides an overview of different sensors used in MoCap and techniques used for estimating human upper limb motion. Findings The existing MoCaps suffer from several issues depending on the type of MoCap used. These issues include drifting and placement of Inertial sensors, occlusion and jitters in Kinect, noise in electromyography signals and the requirement of a well-structured, calibrated environment and time-consuming task of placing markers in multiple camera systems. Originality/value This paper outlines the issues and challenges in MoCaps for measuring human upper limb motion and provides an overview on the techniques to overcome these issues and challenges

Low-Cost Objective Measurement of Prehension Skills

This thesis aims to explore the feasibility of using low-cost, portable motion capture tools for the quantitative assessment of sequential 'reach-to-grasp' and repetitive 'finger-tapping' movements in neurologically intact and deficit populations, both in clinical and non-clinical settings. The research extends the capabilities of an existing optoelectronic postural sway assessment tool (PSAT) into a more general Boxed Infrared Gross Kinematic Assessment Tool (BIGKAT) to evaluate prehensile control of hand movements outside the laboratory environment. The contributions of this work include the validation of BIGKAT against a high-end motion capture system (Optotrak) for accuracy and precision in tracking kinematic data. BIGKAT was subsequently applied to kinematically resolve prehensile movements, where concurrent recordings with Optotrak demonstrate similar statistically significant results for five kinematic measures, two spatial measures (Maximum Grip Aperture – MGA, Peak Velocity – PV) and three temporal measures (Movement Time – MT, Time to MGA – TMGA, Time to PV – TPV). Regression analysis further establishes a strong relationship between BIGKAT and Optotrak, with nearly unity slope and low y-intercept values. Results showed reliable performance of BIGKAT and its ability to produce similar statistically significant results as Optotrak. BIGKAT was also applied to quantitatively assess bradykinesia in Parkinson's patients during finger-tapping movements. The system demonstrated significant differences between PD patients and healthy controls in key kinematic measures, paving the way for potential clinical applications. The study characterized kinematic differences in prehensile control in different sensory environments using a Virtual Reality head mounted display and finger tracking system (the Leap Motion), emphasizing the importance of sensory information during hand movements. This highlighted the role of hand vision and haptic feedback during initial and final phases of prehensile movement trajectory. The research also explored marker-less pose estimation using deep learning tools, specifically DeepLabCut (DLC), for reach-to-grasp tracking. Despite challenges posed by COVID-19 limitations on data collection, the study showed promise in scaling reaching and grasping components but highlighted the need for diverse datasets to resolve kinematic differences accurately. To facilitate the assessment of prehension activities, an Event Detection Tool (EDT) was developed, providing temporal measures for reaction time, reaching time, transport time, and movement time during object grasping and manipulation. Though initial pilot data was limited, the EDT holds potential for insights into disease progression and movement disorder severity. Overall, this work contributes to the advancement of low-cost, portable solutions for quantitatively assessing upper-limb movements, demonstrating the potential for wider clinical use and guiding future research in the field of human movement analysis

Objective Assessment of the Finger Tapping Task in Parkinson's Disease and Control Subjects using Azure Kinect and Machine Learning

Parkinson's disease (PD) is characterised by a progressive worsening of motor functionalities. In particular, limited hand dexterity strongly correlates with PD diagnosis and staging. Objective detection of alterations in hand motor skills would allow, for example, prompt identification of the disease, its symptoms and the definition of adequate medical treatments. Among the clinical assessment tasks to diagnose and stage PD from hand impairment, the Finger Tapping (FT) task is a well-established tool. This preliminary study exploits a single RGB-Depth camera (Azure Kinect) and Google MediaPipe Hands to track and assess the Finger Tapping task. The system includes several stages. First, hand movements are tracked from FT video recordings and used to extract a series of clinically-relevant features. Then, the most significant features are selected and used to train and test several Machine Learning (ML) models, to distinguish subjects with PD from healthy controls. To test the proposed system, 35 PD subjects and 60 healthy volunteers were recruited. The best-performing ML model achieved a 94.4% Accuracy and 98.4% Fl score in a Leave-One-Subject-Out validation. Moreover, different clusters with respect to spatial and temporal variability in the FT trials among PD subjects were identified. This result suggests the possibility of exploiting the proposed system to perform an even finer identification of subgroups among the PD population

Effects of virtual reality associated with serious games for upper limb rehabilitation inpatients with multiple sclerosis: randomized controlled trial

Background: Dexterity and activities of daily living limitations on the upper limb (UL) represent one of the most common problems in patients with multiple sclerosis (MS). The aim of this study was to evaluate the effectiveness of the specially developed Serious Games that make use of the Leap Motion Controller (LMC) as main user interface for improving UL grip muscle strength, dexterity, fatigue, quality of life, satisfaction and compliance. Methods: A single-blinded randomized controlled trial was conducted. The sample was randomized into two groups: an experimental group who received treatment based on serious games designed by the research team using the developed LMC based Serious Games for the UL plus conventional rehabilitation, and a control group who received the same conventional rehabilitation for the UL. Both groups received two 60 min sessions per week over a ten-week period. Grip muscle strength, coordination, speed of movements, fine and gross UL dexterity, fatigue, quality of life, satisfaction and compliance were assessed in both groups pre-treatment, post-treatment and in a follow-up period of 1 month without receiving any treatment. Results: In the experimental group compared to the control group, significant improvements were observed in the post-treatment assessment for coordination, speed of movements, fine and gross UL dexterity. Also, significant results were found in the follow-up in coordination, speed of movements, fine and gross for the more affected side. Conclusions: An experimental protocol using an LMC based Serious Games designed for UL rehabilitation showed improvements for unilateral gross manual dexterity, fine manual dexterity, and coordination in MS patients with high satisfaction and excellent compliance. Trial registration: This randomized controlled trial has been registered at ClinicalTrials.gov Identifier: NCT04171908, Nov 2019.The research leading to these results has received funding from the ROBOHEALTH-A project (DPI2013-47944-C4-1-R) funded by the Spanish Ministry of Economy and Competitiveness

Recent advances in rehabilitation for Parkinson’s Disease with Exergames: A Systematic Review

Objective: The goal of this contribution is to gather and to critically analyze recent evidence regarding the potential of exergaming for Parkinson’s disease (PD) rehabilitation and to provide an up-to-date analysis of the current state of studies on exergame-based therapy in PD patients. Methods: We performed our search based on the conclusions of a previous systematic review published in 2014. Inclusion criteria were articles published in the indexed databases Pubmed, Scopus, Sciencedirect, IEEE and Cochrane published since January 1, 2014. Exclusion criteria were papers with a target group other than PD patients exclusively, or contributions not based on exergames. Sixty-four publications out of 525 matches were selected. Results: The analysis of the 64 selected publications confirmed the putative improvement in motor skills suggested by the results of the previous review. The reliability and safety of both Microsoft Kinect and Wii Balance Board in the proposed scenarios was further confirmed by several recent studies. Clinical trials present better (n = 5) or similar (n = 3) results than control groups (traditional rehabilitation or regular exercise) in motor (TUG, BBS) and cognitive (attention, alertness, working memory, executive function), thus emphasizing the potential of exergames in PD. Pilot studies (n = 11) stated the safety and feasibility of both Microsoft Kinect and Wii Balance Board, potentially in home scenarios as well. Technical papers (n = 30) stated the reliability of balance and gait data captured by both devices. Related metaanalyses and systematic reviews (n = 15) further support these statements, generally citing the need for adaptation to patient’s skills and new input devices and sensors as identified gaps. Conclusion: Recent evidence indicates exergame-based therapy has been widely proven to be feasible, safe, and at least as effective as traditional PD rehabilitation. Further insight into new sensors, best practices and different cognitive stadiums of PD (such as PD with Mild Cognitive Impairment), as well as task specificity, are required. Also, studies linking game parameters and results with traditional assessment methods, such as UPDRS scores, are required. Outcomes for randomized controlled trials (RCTs) should be standardized, and follow-up studies are required, particularly for motor outcomes

Immersive Virtual Reality Training Improved Upper Extremity Function in Patients with Spinal Cord Injuries: A Case Series

Virtual reality (VR) is an emerging treatment tool to engage people in environments that appear and feel similar to real-world objects and events.1 There are various levels of evidence that VR can potentially promote functional activity and neuroplasticity in patients with neurological disorders like spinal cord injury (SCI).2,3 In this case series, we explored the feasibility of using commercially available immersive VR technology as an augmented treatment in the SCI population and compare participant’s suitability for this intervention. Three male SCI participants were recruited in a subacute inpatient rehabilitation facility and participated in VR intervention twice a week in addition to their conventional therapies. Manual strength and functional testing were recorded biweekly until participants discharged. Training includes reaching activities, wrist rotation, gripping, and thumb movement to simulate real-life activities. A questionnaire regarding their experience with VR training was administered at the end. All participants had improvement in strength and functional tests. 9-hole peg test demonstrated clinically meaningful change in two of three participants. Manual muscle test changes were 2, 4.5 and 13.5 points individually. Participants with lower manual muscle test scores at baseline showed more potential to change compared to those who had high scores, which would possibly due to plateau effect. Pinch and grip strength demonstrated small changes which were not clinically important. Participants also rated VR technology of high reality level and great enjoyment in the questionnaire. This case series suggests that immersive VR with head mount display may be viable to provide safe and effective treatment for patients with SCI. VR training appears to be a possible adjunct to physical and occupational therapy as a method of muscle strengthening, improving upper extremity function and improving motivation during subacute rehabilitation

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

The Existing Of Supportive Technology Tools For Hand Motor-Impaired User: A Systematic Literature Review

Diabetes Users who encounter physical and motor impairment persist in struggle to archive the target of performance in the form of hand gesture improvement. Hand gestures are allowed people to give a sign as a communicate medium and to hold, grip and pinch the object. The low ability of hands makes the movement or gesture limited and difficult for them to do the routine activity. This review aim to evaluate the effect of whether the existing supportive technology can assist the hand motor-impairment user. A total of 31 papers were identified and only 10 papers were selected in this review. In this paper, the existing supportive technology tools in the field of motor rehabilitation which is focused on hand motor-impaired users are reviewed. The existing of supportive technology for hand motor-impaired user is not a new field as the paper reviewed from 2014 until 2019. There are few innovations or initiatives from the previous research and study that give a positive effect on the users were identified. Future research is needed to further appreciate and improved the desired role of people with hands motor-impaired in meaningful technology development