SBIR Phase I: Cloud Based Artificial Intelligence for Trend Analysis Using Sensor Data

SBIR 第一阶段：基于云的人工智能，使用传感器数据进行趋势分析

• 批准号: 1622256
• 负责人: Laura Kassovic
• 金额: $ 22.5万
• 依托单位: MbientLab
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622256&HistoricalAwards=false
• 关键词: SBIR; Phase; Cloud; Based; Artificial

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase 1 project is to address the problem of data interpretation, one of the most important and fastest growing issues caused by the influx of wearable technologies. As with all technology, wearable devices are increasing in popularity and decreasing in cost every day. Businesses rushing to catch this wave of technology paradigm are met with the complex problem of how to interpret the data gathered in a way that is accurate and useful to consumers. Many of these businesses are companies with products that were previously completely unrelated with computer or smartphone technologies. As such, they do not have the in-house expertise to not only correctly gather such data, but then analyze it for patterns that could be deemed useful in identifying behavior or conditions for the consumer. By providing a boxed solution that makes machine learning based data analysis possible for the average engineer, our project is aimed to help these businesses cross that hurdle.This Small Business Innovation Research (SBIR) Phase 1 project seeks to bring Artificial Intelligence and Machine Learning systems for use in the hands of non-data/computer scientists. While machine learning and AI techniques are widely used these days in many applications ranging from Google search to Uber rides, they remain fairly esoteric topics with a high learning curve just to understand, let alone apply. We plan to address this issue differently from previous competitors by building a highly intuitive web UI on top of our existing hardware sensor platform. This allows us to leverage the data gathering and processing consistency of our hardware, along with our proprietary SDKs to ensure properly labelled and clean data. As a result, we will have a much easier time developing basic digital processing filters as well as applying machine learning techniques to the data in order to solve generic classification problems.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是解决数据解释问题，这是可穿戴技术涌入造成的最重要和增长最快的问题之一。与所有技术一样，可穿戴设备每天都在日益普及，成本也在不断下降。急于抓住这一技术浪潮的企业遇到了如何以准确和对消费者有用的方式解释收集的数据的复杂问题。其中许多企业的产品以前与计算机或智能手机技术完全无关。因此，他们没有内部的专业知识，不仅可以正确地收集这些数据，而且还可以分析这些数据，以发现可能有助于识别消费者行为或状况的模式。通过提供一个盒装的解决方案，使基于机器学习的数据分析成为可能的一般工程师，我们的项目旨在帮助这些企业跨越这一障碍。这个小企业创新研究（SBIR）第一阶段项目旨在将人工智能和机器学习系统带入非数据/计算机科学家手中。虽然机器学习和人工智能技术如今被广泛应用于从谷歌搜索到Uber乘车等许多应用程序中，但它们仍然是相当深奥的话题，学习曲线很高，只是为了理解，更不用说应用了。我们计划通过在现有的硬件传感器平台上构建高度直观的Web UI来解决这个问题，这与之前的竞争对手不同。这使我们能够利用硬件的数据收集和处理一致性，沿着我们的专有SDK，以确保正确标记和清洁的数据。因此，我们将更容易开发基本的数字处理滤波器，并将机器学习技术应用于数据，以解决通用分类问题。