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

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.