SCH: EXP: Contingent Cueing for the Alleviation of Symptoms of Parkinson's Disease in Ambient Settings

SCH：EXP：在环境环境中缓解帕金森病症状的条件提示

• 批准号: 1502242
• 负责人: Daniel Rucker
• 金额: $ 55.12万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1502242&HistoricalAwards=false
• 关键词: SCH; EXP; Contingent; Cueing; Alleviation

Millions of Americans live with incurable chronic conditions such as stroke, traumatic brain injury, and Parkinson's disease (PD). These degenerative neurological conditions cause difficulty in the performance of daily life activities such as walking and self-care, limit independence, and lead to depressed quality of life. There are currently no quantitative tools that allow us to understand how these individuals are affected by their conditions on a daily basis, and how rehabilitation and medication affect these people in the long term. The advent of novel technologies such as smart phones and smart watches facilitates exciting new opportunities. These tools allow us to quantitatively monitor, and to positively affect the behaviors of such individuals. This award supports research that will determine how to measure the movements and behaviors of these individuals, and then tests a technique that uses real-time feedback and stimuli to minimize the disease symptoms. We evaluate our approach in individuals suffering from PD, a particularly debilitating disease. Research results will provide much richer information on how people respond to therapy and medication, and may prove useful in other populations of people aging with and into disability. The approach, which integrates engineering and medicine, will help broaden the participation of underrepresented groups in science, technology, engineering, and mathematics research.Wearable sensing and telehealth are promising tools for revolutionizing healthcare outside of the clinical setting. However, the adaptation of these technologies to specific populations in remote settings remains a challenge. The current research approach focuses on individuals living with the chronic effects of Parkinson's Disease (PD). We propose a system that combines wearable sensors and smart phones to assist these individuals with a particularly debilitating syndrome, freezing of gait. This research will fill the knowledge gap of how to monitor and affect the symptoms of these individuals in ambient settings. We leverage electronic sensor design, machine learning, and signal processing approaches to: (1) determine user-specific models of movement from worn sensor data; (2) develop algorithms to predict onset of user symptoms; and (3) provide context-dependent external stimuli to alleviate symptoms. These methods may generalize to other individuals living with the chronic disability.

数百万美国人患有无法治愈的慢性疾病，如中风，创伤性脑损伤和帕金森病（PD）。这些退行性神经系统疾病导致日常生活活动（如行走和自我护理）的表现困难，限制独立性，并导致生活质量下降。目前还没有定量工具可以让我们了解这些人每天如何受到他们的病情的影响，以及康复和药物治疗如何长期影响这些人。智能手机和智能手表等新技术的出现带来了令人兴奋的新机遇。这些工具使我们能够定量监测，并积极影响这些人的行为。该奖项支持的研究将确定如何测量这些人的运动和行为，然后测试一种使用实时反馈和刺激来最大限度地减少疾病症状的技术。我们在患有PD的个体中评估我们的方法，PD是一种特别衰弱的疾病。研究结果将提供更丰富的信息，说明人们如何对治疗和药物作出反应，并可能证明对其他老年人和残疾人有用。该方法将工程学和医学结合起来，将有助于扩大科学、技术、工程学和数学研究中代表性不足的群体的参与。可穿戴传感和远程医疗是临床环境之外革命性医疗保健的有前途的工具。然而，使这些技术适应偏远地区的特定人群仍然是一个挑战。目前的研究方法主要集中在帕金森病（PD）慢性影响的个体。我们提出了一个系统，结合可穿戴传感器和智能手机，以帮助这些人特别衰弱的综合征，冻结的步态。这项研究将填补如何在环境中监测和影响这些人的症状的知识空白。我们利用电子传感器设计、机器学习和信号处理方法来：（1）从佩戴的传感器数据中确定用户特定的运动模型;（2）开发预测用户症状发作的算法;（3）提供依赖于上下文的外部刺激来缓解症状。这些方法可以推广到其他患有慢性残疾的人。