I-Corps: A Smart Technology for Child Safety

I-Corps：儿童安全智能技术

• 批准号: 1916305
• 负责人: Srinivas Katkoori
• 金额: $ 5万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916305&HistoricalAwards=false
• 关键词: Corps; Smart; Technology; Child; Safety

The broader impact/commercial potential of this I-Corps project is focused on a smart technology that will help smart tethering between elements of social dyads such as parents/infants, caregivers/ patients, or even owners/pets. Given a complex set of scenarios in which such dyads operate, in some subset of scenarios alerts must be generated, while in other complex scenarios no alerts should be generated. This project employs machine learning techniques to propose a robust technology that minimizes false alarm rate. The system will automatically learn about the environment (unsupervised learning) to minimize the false alarm rate. The smart tethering system consists of hardware and smart software algorithms. Possible applications include child monitoring, patient monitoring and possibly other safety-motivated interventions.This I-Corps project will further develop a smart tethering technology that integrates sensors, computing platforms, and artificial intelligence. The proposed technology implements a combination of genetic algorithm (GA) and deep learning approaches to model the environment to minimize the response error. Initially the system is trained with training data (GA coupled with deep learning) and then deployed in user's environment. After deployment, the system smartly learns complex scenarios and customizes to the user's environment based on unsupervised learning approach. The project builds upon prior work in hardware development for smart adaptive systems. In general, for smart solutions, researchers mostly have employed supervised learning (training based) due to its simplicity. If this research is successful, it will advance the state-of-the-art in the unsupervised learning approach as well as GA based smart systems.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.

这个I-Corps项目的更广泛的影响/商业潜力集中在一种智能技术上，这种技术将有助于在父母/婴儿、看护者/患者甚至主人/宠物等社会二元体之间进行智能拴系。 给定一组复杂的场景，在这些场景中，必须生成警报，而在其他复杂场景中，不应生成警报。 该项目采用机器学习技术，提出了一种鲁棒的技术，最大限度地减少误报率。 系统将自动学习环境（无监督学习），以最大限度地降低误报率。 智能网络共享系统由硬件和智能软件算法组成。可能的应用包括儿童监护、病人监护和其他可能的安全干预。这个I-Corps项目将进一步开发集成传感器、计算平台和人工智能的智能网络共享技术。所提出的技术实现了遗传算法（GA）和深度学习方法的组合，以对环境进行建模，以最大限度地减少响应误差。 最初，系统使用训练数据（GA与深度学习相结合）进行训练，然后部署在用户环境中。 部署后，系统智能地学习复杂的场景，并基于无监督学习方法定制用户的环境。该项目建立在智能自适应系统硬件开发的基础上。 一般来说，对于智能解决方案，研究人员大多采用监督学习（基于训练），因为它很简单。如果这项研究取得成功，它将推动最先进的无监督学习方法以及基于GA的智能系统。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。