Augmented reality system for control of parkinson's symptoms

帕金森氏症影响了大约10万加拿大人。帕金森氏症患者的震颤和其他不自主运动严重降低了他们的生活质量，这是由大脑中的化学物质不平衡引起的。具体来说，缺乏神经化学物质多巴胺会导致从大脑到肌肉的错误负反馈信号，最终阻碍自主运动。博士之间的新合作。Meeko Oishi、Martin McKeown和Z. Jane Wang分别是混合（离散/连续）系统控制、帕金森病神经学和生物医学信号处理方面的专家，他们的目标是开发一种新的互补替代方案。这里所要求的设备将使对帕金森患者的视觉刺激、大脑活动和运动控制之间的反馈进行新的研究成为可能。本提案中要求的增强现实系统将包括一个新的无创、非药物治疗帕金森病症状的试验台。通过增强现实耳机提供的视觉刺激将用于中断脑电图（EEG）测量推断的大脑信号，这些信号会阻碍帕金森病患者的正常运动。设计有效合成视觉输入信号的控制律需要合理的神经系统数学模型，以及对患者的神经和身体状态进行有意义的测量。为此，将首先设计神经学实验来推断视觉刺激和自主运动之间的关系。生物医学信号处理可以从海量数据中提取相关的生物信息。最后，利用混合控制理论技术，基于神经学模型和提取的医学数据合成控制输入。

Parkinson's disease affects approximately 100,000 Canadians. Tremors and other involuntary movements in Parkinson's patients seriously degrade their quality of life, and are caused by a chemical imbalance in the brain.  Specifically, a lack of the neurochemical dopamine results in a faulty negative feedback signal from the brain to the muscles, which ultimately hinders voluntary movement.  A new collaboration between Drs. Meeko Oishi, Martin McKeown, and Z. Jane Wang, experts in control of hybrid (discrete/continuous) systems, neurology of Parkinson's disease, and biomedical signal processing, respectively, aims to develop a novel complementary alternative.  The equipment requested here will enable new research into the feedback between visual stimuli, brain activity, and motor control in Parkinson's patients. The augmented reality system requested in this proposal will comprise a test-bed for novel non-invasive, non-pharmacological treatment of the symptoms of Parkinson's disease.  Visual stimuli, provided through an augmented reality headset, will be used to interrupt brain signals -- inferred from electroencephalography (EEG) measurements -- that hinder normal movement in patients with Parkinson's disease.    Designing a control law to effectively synthesize visual input signals requires a reasonable mathematical model of the neurological system, as well as meaningful measurements regarding the patient's neurological and physical state.  To this end, neurological experiments will be first designed to deduce the relationship between visual stimuli and voluntary movement.  Biomedical signal processing will enable extraction of relevant biological information from a massive set of data.  Lastly, the control input will be synthesized based on the neurological model and the extracted medical data, using techniques from hybrid control theory.