Inspiration
The intersection between human emotion and technology has been rapidly expanding, providing Data Science and Computer Science majors such as us to explore and push the boundaries of our collective knowledge through new technologies that grow and improve everyday.
What it does
Midas uses a webcam to process data about traders' facial expressions in real-time, creating and contributing to a user database of processed statistical information about their emotions during their different trades. By pairing information about the trades, their outcomes, and the emotions exhibited throughout, a LLM then generates custom feedback for each user to improve their performance based on their personal emotional data.
How we built it
Midas was built using a combination of Hume.AI and LLM training. We used Python for the backend as well as front end using REFLEX. Hume was used to get emotional data and face tracking. MindsDB was use for SQL database integration into our LLM.
Challenges we ran into
Developing an accurate reading of someone's emotions over a period of time and how exactly to train the LLm based on these emotions. Additionally, ensuring user privacy and data security was a concern.
Accomplishments that we're proud of
We successfully created a functional system to analyze traders' emotions during their trades. Integrating emotional data with trade-related information is a unique feature of our project. We're proud of our LLM's ability to generate personalized feedback based on a user's emotional data.
What we learned
We gained a deeper understanding of emotion tracking, Natural Languge Processing, and data analysis techniques. Additionally, we learned about the challenges and ethical considerations involved in working with user data and privacy.
What's next for Midas
There are several potential future directions for Midas. This might include expanding the types of emotional data collected, conducting user studies to assess the impact of emotional feedback on trading performance, and enhancing the overall user experience. We may also explore opportunities to integrate with trading platforms or provide more detailed analytics for traders. Further development could involve ensuring regulatory compliance in the finance industry and addressing potential privacy concerns. Additionally, we could expand the use of the technology beyond trading, such as in education or mental health applications.
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