Augmented reality system for control of parkinson's symptoms

帕金森氏症影响着大约10万加拿大人。帕金森氏症患者的震颤和其他非自主运动严重降低了他们的生活质量，是由大脑中的化学物质失衡引起的。具体地说，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。大石美子博士、马丁·麦基翁博士和Z·简·王博士分别控制混合(离散/连续)系统、帕金森病神经学和生物医学信号处理，旨在开发一种新的互补替代方案。这里要求的设备将使对视觉刺激、大脑活动和生物医学信号处理之间的反馈的新研究成为可能帕金森氏症患者的运动控制。这项提案中要求的增强现实系统将包括一个试验台，用于对帕金森病症状进行新的非侵入性、非药物治疗。通过增强现实耳机提供的视觉刺激将被用来中断大脑信号--从脑电(EEG)测量中推断--阻碍帕金森病患者的正常运动。设计一种控制规律以有效地合成视觉输入信号需要一个合理的神经系统数学模型，以及关于患者神经和身体状态的有意义的测量。为此，神经学实验将首先设计来推断视觉刺激和随意运动之间的关系。生物医学信号处理将使人们能够从大量数据中提取相关的生物信息。最后，将基于神经模型和提取的医学数据，使用混合控制理论的技术来合成控制输入。

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.