STTR Phase I: Wearable System for Mining Parkinson's Disease Symptom States in an Ambulatory Setting

这项小型企业技术转让（STTR）第一阶段项目的更广泛影响/商业潜力是减轻帕金森病患者和老年人的跌倒风险，这将可能为家庭和患者每年节省340亿美元的跌倒相关伤害和康复费用。跌倒在65岁及以上的人群中很常见，三分之一的人在一个日历年中至少跌倒一次，帕金森病患者跌倒的可能性是同龄人的两倍。预计所提出的技术将提供患者健康的整体视图。集成传感器检测到的实时数据提供了消费者和护理人员可以用来在家里和医生一起规划健康策略的信息。预计所提出的技术将无缝融入消费者的生活，使他们受益于技术的力量，而不会难以利用它。目前市场上有几种跌倒检测系统;然而，这些系统的两个主要问题是合规性和检测。没有现有的跌倒预防装置确定患者跌倒的风险何时升高。该技术有望进入个人应急响应系统（PERS）市场，预计到2017年，该市场将增长至18.6亿美元。该项目旨在满足消费者对慢性健康症状的监测和主动性需求，特别是与福尔斯有关的症状。拟议项目的目标是开发和商业化一种设备，预测何时可能发生跌倒，并提供可操作的反馈。我们将使用机器学习来实现拟议的研究目标，方法是分析从嵌入在背部支架中的传感器收集的数据，以开发预测症状发作并提醒帕金森病患者和护理人员跌倒风险增加的算法。开发的算法来分析从传感器收集的数据，对所提出的技术是该项目的智力价值。机器学习算法将用于找到传感器读数与个人正在经历的症状之间的相关性。预期的结果是，数据中发现的相关性将有助于更好地了解个人的症状，疾病进展以及哪些传感器读数表明跌倒风险增加。了解导致帕金森氏症患者跌倒的机制将开发更好的警报系统并改善跌倒预防。数据收集和分析的结果也可以更好地检测帕金森病进展的早期预警信号。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to mitigate fall risk among Parkinson's patients and the elderly, which will potentially save families and patients $34 billion annually in fall-related injuries and rehabilitation. Falling is common among individuals age 65 and over, one in three people fall at least once in a calendar year and Parkinson's patients are twice as likely to fall as their counterparts. It is expected that the proposed technology will provide a holistic view of a patient's health. The real-time data detected by the integrated sensors offers information that consumers and caretakers can use to plan health strategies at home and with their physicians. The proposed technology is expected to fit seamlessly into the lives of consumers so that they benefit from the power of technology without the difficulty utilizing it. Several fall detection systems are currently on the market; however, the two main issues with these systems are compliance and detection. No existing fall prevention devices determine when a patient's risk of falling is elevated. The proposed technology has the potential to enter the personal emergency response systems (PERS) market, which is estimated to grow to $1.86 billion by 2017.The proposed project addresses consumers' need to monitor and be proactive about their chronic health symptoms, particularly as they relate to falls. The goal of the proposed project is to develop and commercialize a device that predicts when a fall is likely to occur and to provide actionable feedback. We will use machine learning to achieve the proposed research objective by analyzing data collected from sensors embedded in a back brace to develop algorithms that will predict symptom onset and alert Parkinson's patients and caretakers to increased fall risk. The algorithms developed to analyze the data collected from the sensors on the proposed technology are the intellectual merit of this project. The machine learning algorithms will be used to find a correlation between sensor readings and symptoms the individual is experiencing. The anticipated results are that the correlations found in the data will lead to a better understanding of the individual's symptoms, disease progression, and which sensor readings indicate increased fall risk. Understanding the mechanisms that cause individuals with Parkinson's to fall will develop better alert systems and improve fall prevention. The results from data collection and analysis could also lead to better detection of early warning signs of Parkinson's progression.