Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation

Objective: To apply deep learning pose estimation algorithms for vision-based assessment of parkinsonism and levodopa-induced dyskinesia (LID). Methods: Nine participants with Parkinson's disease (PD) and LID completed a levodopa infusion protocol, where symptoms were assessed at regular intervals using the Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating Scale (UPDRS). A state-of-the-art deep learning pose estimation method was used to extract movement trajectories from videos of PD assessments. Features of the movement trajectories were used to detect and estimate the severity of parkinsonism and LID using random forest. Communication and drinking tasks were used to assess LID, while leg agility and toe tapping tasks were used to assess parkinsonism. Feature sets from tasks were also combined to predict total UDysRS and UPDRS Part III scores. Results: For LID, the communication task yielded the best results for dyskinesia (severity estimation: r = 0.661, detection: AUC = 0.930). For parkinsonism, leg agility had better results for severity estimation (r = 0.618), while toe tapping was better for detection (AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741 and 0.530, respectively. Conclusion: This paper presents the first application of deep learning for vision-based assessment of parkinsonism and LID and demonstrates promising performance for the future translation of deep learning to PD clinical practices. Significance: The proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD.Comment: 8 pages, 1 figure. Under revie

Video-Based Analyses of Parkinson's Disease Severity: A Brief Review

Remote and objective assessment of the motor symptoms of Parkinson's disease is an area of great interest particularly since the COVID-19 crisis emerged. In this paper, we focus on a) the challenges of assessing motor severity via videos and b) the use of emerging video-based Artificial Intelligence (AI)/Machine Learning techniques to quantitate human movement and its potential utility in assessing motor severity in patients with Parkinson's disease. While we conclude that video-based assessment may be an accessible and useful way of monitoring motor severity of Parkinson's disease, the potential of video-based AI to diagnose and quantify disease severity in the clinical context is dependent on research with large, diverse samples, and further validation using carefully considered performance standards

Characterizing the Effects of High-intensity Exercise on Balance and Gait under Dual-task Conditions in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder, characterized by four cardinal motor symptoms including bradykinesia, tremor, rigidity, and postural instability, and non-motor symptoms including cognitive impairment. Daily activities, such as walking and maintaining balance, are impacted due to impairments in motor function, and are further exacerbated with the addition of cognitive loading, or dual-tasking (DT). High-intensity exercise has demonstrated centrally-mediated improvements of PD symptoms, with additional positive effects on overall health. The goal of this project was to identify changes in dynamic balance recovery and gait function under conditions with and without increased cognitive load after a high-intensity exercise intervention in a PD population. Participants included people with PD who completed an eight-week cycling intervention (PDE), people with Parkinson’s disease who did not complete the intervention (PDC), and healthy age-matched controls (HC), with 14 subjects per group. In Aim 1, while participants underwent a series of destabilizing balance tests, the time taken to regain balance and the center of pressure movement during balance recovery were measured. The PDE group demonstrated greater improvement in balance recovery after exercise compared with the PDC group. In Aim 2, participants completed a series of gait and cognitive tasks, both separately and concurrently. Outcome measures included spatiotemporal and kinematic gait parameters of the lower and upper extremities. The PDE group demonstrated significant improvement in gait measures and DT abilities compared to PDC, while no changes were found in cognitive function for any group. The standard clinical methods of measuring motor function can be subjective, and may not capture subtle motor characteristics. Force plate and motion-capture technologies can provide detailed, objective outcome data, therefore improving the understanding of how exercise affects motor symptoms of Parkinson’s disease. The Motek Computer Assisted Rehabilitation Environment (CAREN) system at the Cleveland Clinic was used to create the testing environment and for data collection. These results of this project suggest global changes in motor function demonstrated by changes in balance recovery and lower and upper extremity gait function. Quantitative gait analysis has shown to be an important metric in assessing effectiveness of an exercise intervention in PD

Characterizing the Effects of High-intensity Exercise on Balance and Gait under Dual-task Conditions in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder, characterized by four cardinal motor symptoms including bradykinesia, tremor, rigidity, and postural instability, and non-motor symptoms including cognitive impairment. Daily activities, such as walking and maintaining balance, are impacted due to impairments in motor function, and are further exacerbated with the addition of cognitive loading, or dual-tasking (DT). High-intensity exercise has demonstrated centrally-mediated improvements of PD symptoms, with additional positive effects on overall health. The goal of this project was to identify changes in dynamic balance recovery and gait function under conditions with and without increased cognitive load after a high-intensity exercise intervention in a PD population. Participants included people with PD who completed an eight-week cycling intervention (PDE), people with Parkinson’s disease who did not complete the intervention (PDC), and healthy age-matched controls (HC), with 14 subjects per group. In Aim 1, while participants underwent a series of destabilizing balance tests, the time taken to regain balance and the center of pressure movement during balance recovery were measured. The PDE group demonstrated greater improvement in balance recovery after exercise compared with the PDC group. In Aim 2, participants completed a series of gait and cognitive tasks, both separately and concurrently. Outcome measures included spatiotemporal and kinematic gait parameters of the lower and upper extremities. The PDE group demonstrated significant improvement in gait measures and DT abilities compared to PDC, while no changes were found in cognitive function for any group. The standard clinical methods of measuring motor function can be subjective, and may not capture subtle motor characteristics. Force plate and motion-capture technologies can provide detailed, objective outcome data, therefore improving the understanding of how exercise affects motor symptoms of Parkinson’s disease. The Motek Computer Assisted Rehabilitation Environment (CAREN) system at the Cleveland Clinic was used to create the testing environment and for data collection. These results of this project suggest global changes in motor function demonstrated by changes in balance recovery and lower and upper extremity gait function. Quantitative gait analysis has shown to be an important metric in assessing effectiveness of an exercise intervention in PD

Personalized Medicine for Parkinson's Disease

Personalized medicine for Parkinson’s disease is a growing and emerging concept in light of recent recognition that Parkinson’s is a syndromic condition affecting multiple neurotransmitter systems, as well as brain and extracranial structures. The clinical expression is, thus, heterogeneous, and presentation age can range from the 30s to the 90s, with PD in older patients being associated with significant neuropathological comorbidity as well, involving not just misfolded alpha synuclein deposition but also amyloid and tau. Traditional and largely guideline-driven “one size fits all” management strategies adopted in clinical practice are, therefore, often inadequate in holistic management of a patient, particularly when aspects of motor and nonmotor symptoms are taken into consideration. In this supplement of JPM, we present a selection of papers which address several possible strands of personalized medicine in PD, ranging from genomic precision medicine to digital “checklists” to ensure delivery of holistic personalized medicine involving nonpharmacological strategies, as well. We are soliciting any papers addressing biomarkers, genetics and pharmacogenetics, treatment or complementary therapies for personalized or individualized treatment for PD

Investigation of Visual Perceptions in Parkinson\u27s Disease and the Development of Disease Monitoring Software

Non-motor Parkinson’s Disease (PD) symptoms are substantial factors of PD arising throughout disease stages, yet their diagnosis and monitoring remain a challenge. Sensory abnormalities in PD occur across sensory systems and disease stages, contributing to disease-related impairments. However, the extent of symptoms is unknown, with inadequate monitoring and treatment options furthering disease management difficulties. The current work studies movement-independent visual perceptions of time, displacement and velocity in PD patients across disease stages using levodopa, deep brain stimulation (DBS), or no PD therapy. Perceptual tasks were conducted using a computer-generated graphical device designed with a focus on simplicity and flexibility. Perception of all tested visual modalities was impaired in PD (often extending to early PD stages), with negligible levodopa and DBS induced improvement. The observations help explain visuospatial, visual recognition and timing deficits occurring in PD while providing potential disease markers, and validates the graphical tool’s usefulness for disease diagnosis and monitoring

A review of computer vision-based approaches for physical rehabilitation and assessment

The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered

Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers

17 pages, 1 figure, 3 tablesPreprin

New Insights into Molecular Mechanisms Underlying Neurodegenerative Disorders

Neurodegenerative disorders encompass a broad range of sporadic and/or familial debilitating conditions characterized by the progressive dysfunction and loss of selective neuronal populations, determining different clinical phenotypes. Emerging research data indicate an interplay of genetic factors and epigenetic mechanisms underlying neurodegenerative processes, which lead to increased prevalence of neurodegenerative disorders. In concert with the constant increase in the aging population, neurodegenerative disorders currently represent a major challenge to public health worldwide. Despite recent advances in clinical and preclinical research, the pathogenesis of these disorders still remains poorly understood, without effective treatments being available to halt the neurodegenerative processes, but rather aiming at relieving symptoms. Therefore, a critical evaluation of current research data and in-depth understanding of the molecular mechanisms that lead to neurodegeneration are crucial in order to identify potential therapeutic targets that can pave the way to the development of novel and promising therapies. This Special Issue is focused on novel molecular data in the field of neurodegeneration that associate with the onset and progression of neurodegenerative diseases. We are particularly interested in original articles and reviews that provide new insights into the main molecular pathogenic mechanisms underlying neurodegenerative disorders, aiming to identify potential biomarkers and novel therapeutic strategies