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移（STTR）第一阶段项目的更广泛影响/商业潜力是降低帕金森患者和老年人的跌倒风险，这可能会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，三分之一的人每年至少跌倒一次，帕金森氏症患者摔倒的可能性是同龄人的两倍。预计拟议的技术将提供病人健康的整体视图。集成传感器检测到的实时数据提供信息，消费者和护理人员可以使用这些信息在家中或与医生一起规划健康策略。这项拟议中的技术有望无缝融入消费者的生活，使他们从技术的力量中受益，而不会有使用它的困难。目前市场上有几种跌落检测系统；然而，这些系统的两个主要问题是遵从性和检测。目前还没有任何预防跌倒的设备可以确定患者何时会有跌倒的风险。该技术有望进入个人应急响应系统（PERS）市场，预计到2017年该市场将增长到18.6亿美元。拟议的项目解决了消费者监测和主动关注自己的慢性健康症状的需求，特别是与跌倒有关的症状。该项目的目标是开发并商业化一种设备，该设备可以预测何时可能发生跌倒，并提供可操作的反馈。我们将利用机器学习来实现提出的研究目标，通过分析从嵌入在背部支架中的传感器收集的数据，开发算法来预测症状的发作，并提醒帕金森患者和护理人员增加跌倒的风险。基于所提出的技术开发的用于分析从传感器收集的数据的算法是本项目的智力优点。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，在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有助于开发更好的警报系统，并改善预防跌倒的措施。数据收集和分析的结果也可能有助于更好地发现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.