AI Video Review
I completed a 7-week AI product design course, diving into fundamental AI principles and their applications in user-centered design. The program covered machine learning, deep learning, and ethical considerations, with hands-on assignments focusing on real-world AI integration and predictive design.
LucidFlow addresses the challenge wrestlers face in analyzing and critiquing their matches effectively. Users upload match videos, and the AI transcribes the action, classifies moves using random forests, clusters similar matches for pattern recognition, and utilizes NLP, sentiment analysis, collaborative filtering, and reinforced learning for comprehensive analysis. This provides users with actionable insights, alternative moves, and strategies, improving their overall performance.
LucidFlow
Challenge
Wrestlers often struggle to effectively analyze and critique their matches. The lack of a systematic approach results in challenges such as identifying patterns, understanding opponents' strategies, and translating insights into actionable improvements.
User Contribution
Value Proposition
Key Features:
Match Upload: Users can easily upload their wrestling match videos to the platform.
AI Transcription: LucidFlow's AI transcribes the match action, creating a textual representation of the match.
Move Classification: Using random forests, the AI classifies wrestling moves, positions, and situations within the match.
Pattern Recognition: Clustering similar matches enables pattern recognition, helping users identify recurring strategies.
NLP Integration: Natural Language Processing (NLP) identifies terms within the match transcript, enhancing analysis accuracy.
Sentiment Analysis: The AI gauges user reactions to the analysis and recommendations, providing insights into user satisfaction.
Collaborative Filtering: Based on similar matches, collaborative filtering recommends alternative moves and strategies to users.
Reinforced Learning: The AI's decision-making process improves through reinforced learning, adapting to user feedback and match outcomes.
Image Recognition: Deep learning techniques enable the AI to recognize and analyze match footage.
Personalized Feedback: Users receive personalized feedback and insights based on the analysis of their matches, aiding in skill development and strategy refinement.
Uploaded Matches
User either clicks on the match, or filters to see matches from a particular wrestler. User can also click “Head to Head’ to see an AI generated analysis of how the wrestler stacks up in a head-to-head match.
Transcript
User can watch the match, move by move to get a visual of the amount of action from each wrestler. They can also click on any time stamp to review that part.
Editable Transcript
User can review the automated transcript of the action and add, remove, or change any piece of the action if it is inaccurate. That information is tagged and stored on the Data page.
Head-to-Head
User chooses the wrestler to compare in a head-to-head. Fields populate with each wrestler’s strengths, weaknesses, opportunities for primary wrestler, and threats of opponent -- all based off of historical matches and pattern recognition.
Data
Conclusion
LucidFlow's Match Analysis Feature revolutionizes wrestling education by providing a sophisticated yet user-friendly solution. The combination of AI technologies, collaborative user engagement, and a thoughtful UX design ensures that wrestlers, coaches, and parents can elevate their understanding of the sport, ultimately improving performance and fostering a stronger wrestling community.