ELC Idea Track - Motion and Co-ordinate Detection

Inspiration

Our insights into various issues stem from one-to-one interviews, vlogs, and research papers. A common theme seemed to be that those with disabilities for a long time already had hacks and mostly needed pointers on precision, and those who have recently been disabled need guidance on how. This along with the fact that we wouldn’t have to exclude an existing market segment guided our solutioning. What we are looking to achieve is professional and inclusive beauty for all.

What it does

Our solutioning considers a motion and co-ordinate detecting model addition to the existing VMA app. This model would detect where the person’s arm or foot or even an applicator is, and tell where to position it, and how to move it so that the user achieves something they want.

How we built it

We used a tensorflow model for this which was open source, but to give additional directions, there would also have to be a desired list of looks a person can choose from, and a context sensing chatbot which will have instructions on how to go about achieving it. Although this is primarily for a visually impaired person, it could be easily scalable to someone with multiple disabilities, or motor function disabilities.

Challenges we ran into

Customizing the solution to cater to a large range of end-users with existing apps.

Accomplishments that we're proud of

Addressing both the pre-and post-application phases of the customer journey

What we learned

Current solutions in the market are very limited and there is an untapped market for the visually impaired and hearing/speech impaired.

What's next for ELC Idea Track - Motion and Co-ordinate Detection

Fully developed in-app buying experience, and introducing low contrast image detection (like mild make-up) to enable nuanced effects which is currently not part of the solution.

Built With

• tensorflow