I-Corps: A Wearable, Biomarker-Tracking Device Platform Using Machine Learning and Predictive Technology for Positive Behavior Change

这个i-Corps项目的更广泛的影响/商业潜力围绕着人脑-S倾向于有害于人类健康和福祉的物质，如危险数量的糖、盐和药物，这正在造成全球健康和安全风险。无法避免这些诱惑会缩短寿命，增加医疗成本，并使资源紧张。该团队将使用机器学习和模式识别人工智能来识别导致人类破坏性行为的因素并做出反应。虽然我们产品的最初意图是为潜在客户提供一种工具，以检测和防止吸毒过量和复发，提供受监督个人的干预，节省应急资源，并节省纳税人的钱，但在随后的迭代中，这种无监督的学习人工智能可以用于识别任何行为并对其做出反应--例如不安全驾驶、暴饮暴食或愤怒控制。帮助人们识别他们行为的前兆可以作为一种生物反馈，为他们和他们的支持网络提供及时和个性化的洞察、预防和干预--其中一些可以通过支持物联网的设备(例如锁车？S点火；呼叫赞助商；播放平静的音乐等)来实现，从而在许多人群中产生成本和健康效益。这个i-Corps项目专注于一个正在申请专利的平台，通过使用可穿戴设备和人工智能对正在康复的人进行及时的干预，预测和防止成瘾复发和吸毒过量。过去和现在的研究都支持检测渴望状态，收集吸毒者的智能手机使用和生物识别可穿戴数据也是有科学依据的。研究人员结合使用智能手机和可穿戴设备的数据来预测酗酒行为，我们计划将这项研究应用于阿片类药物的使用。此外，可卡因和阿片类药物的研究使用与我们的研究相同的可穿戴设备。我们通过将对阿片类药物使用障碍恢复期的人进行及时的基于智能手机的干预与模式检测预测模型相结合来扩展当前的研究，该模型将在人群水平上进行训练，并在个人水平上进行改进。我们将收集决定药物复发风险状态的所有相关因素的数据：生理、智能手机使用、位置数据、自我报告和支持网络报告。一旦检测到高风险状态，基于智能手机应用程序的干预将以及时和可定制的方式实施。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project revolves around the human brain?s proclivity towards substances detrimental to human health and wellbeing, such as dangerous amounts of sugar, salt, and drugs, which is causing global health and safety risks. Inability to avoid these temptations shortens lifespans, increases healthcare costs, and strains resources. This team will use machine learning and pattern recognition AI to identify and react to factors that result in destructive human behaviors. While the initial intent of our product is to provide potential customers with a tool to detect and prevent addiction overdose and relapse, offer interventions of supervised individuals, conserve emergency response resources, and save taxpayer money, this unsupervised learning AI could, in subsequent iterations, be used to identify and react to any behavior -- such as unsafe driving, binge eating, or anger management. Helping people identify the precursors to their behaviors could serve as a type of biofeedback that gives them and their support networks timely and individualized insights, preventions, and interventions -- some of which could be implemented by Internet of Things enabled devices (e.g. locking a car?s ignition; calling a sponsor; playing calm music; etc.), resulting in cost and health benefits across many populations.This I-Corps project is focused on a patent-pending platform to predict and prevent addiction relapses and overdoses by treating those in recovery with timely interventions using wearable devices and artificial intelligence. Detecting craving states is supported by past and current research, and there is scientific justification for gathering smartphone usage and biometric wearable data on drug users. Researchers have used the combination of smartphone usage and wearables data to predict binge drinking behavior and we plan to apply this research to opioid use. Additionally, cocaine and opioid research uses the same wearable device as our studies. We expand upon current research by combining just-in-time smartphone-based interventions for people in recovery for opioid use disorders with pattern-detection predictive models that will be trained at the population level and refined at the individual level. We will gather data on all relevant factors that determine drug relapse risk state: physiology, smartphone usage, location data, self-reports, and support-network-reports. Once a high-risk state is detected, a smartphone app-based intervention focused on behavior change will then be implemented in a timely and customizable manner.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.