Augmented analysis and data collection for materials design and evaluations

Considerations: Additional considerations include evaluation of wearable technology that will enhance work, and minimize distraction. One of the resources listed under the FTE has initiated some research in this area, and could be leveraged to support this effort. \nConsiderations also include the development of an IoT network, approved and vetted by IT Security, which allows for the development of the proof of concept. In order for this project to succeed, it is critical that IT Security supports the project, and provides security input into the development of the environment and solution. The use of wearable tech is still a fairly new concept on center, so there may be a high lift in order to implement a proof of concept. \nThe environment that will house the data, run the learning model, and host the application is an additional consideration. In theory all of this can be managed in a cloud environment, however due to security considerations, or the data steward's input, it may be necessary to implement a hybrid cloud environment. \n\nOpportunities: \nIf the proof of concept proves valid, the use of wearable technology can be expanded across other areas of interest:\n\nLook before you dig: The wearable tech would show the user a map of underground infrastructure, on location. \nOr the user could access training material, while on site, for the item requiring maintenance. This model could also be applied to physical IT system maintenance.

Develop a proof of concept to utilize wearable technology to capture visual and audio data during materials analysis. The subject matter expert (SME) would wear a pair of virtual reality/AR glasses which would capture images and audio of the materials evaluation process. The collected data would be utilized to inform and train a model which then augments the human expert's knowledge when conducting materials analysis. \nThe goal is to create a model that continues to learn based upon input from the wearable technology, and provides recommendations and input to the SME who conducts the analysis. Incremental versions of machine learning models can be used in on-line applications to facilitate in-time updates to parameters within the model. When a human user encounters events known to a machine learning model during visual inspection with an Augmented Reality (AR) system, the output/feedback of the tool results in improved human understanding. But, if a human user encounters an unknown event, they can provide expert feedback that ultimately augments the machine learning model through a wireless workflow

This is early/mid-stage (TRL 1) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.