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

这个小型企业技术转移(STTR)第一阶段项目的更广泛影响/商业潜力是降低帕金森氏症患者和老年人的跌倒风险，这可能每年为家庭和患者节省340亿美元的跌倒相关伤害和康复费用。跌倒在65岁及以上的人中很常见，三分之一的人一年至少跌倒一次，帕金森患者跌倒的可能性是他们的同龄人的两倍。预计这项拟议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照顾者可以用来在家中和与医生一起计划健康策略的信息。预计这项拟议的技术将无缝地融入消费者的生活，这样他们就可以从技术的力量中受益，而不会遇到使用它的困难。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。现有的预防跌倒设备还没有确定患者何时跌倒的风险增加。这项拟议的技术有可能进入个人应急系统(PERS)市场，预计到2017年，该市场将增长到18.6亿美元。拟议的项目旨在满足消费者监测和积极应对其慢性健康症状的需求，特别是与跌倒有关的症状。拟议项目的目标是开发一种设备，并将其商业化，该设备可以预测何时可能发生坠落，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，方法是分析从嵌入后支架的传感器收集的数据，以开发预测症状出现并提醒帕金森患者和护理人员跌倒风险增加的算法。开发的用于分析从传感器收集的数据的算法是该项目的智能优势。机器学习算法将被用来找到传感器读数和个体正在经历的症状之间的关联。预期的结果是，数据中发现的相关性将导致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出更好的警报系统，并提高预防跌倒的能力。数据收集和分析的结果也可能导致更好地检测帕金森进展的早期预警迹象。

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.