Ryan phillips week4_demo_noname
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Ryan Phillips created a wearable technology system to provide boxers with feedback on their form during workouts. The system uses motion sensors to collect raw data on punches, which is then analyzed using machine learning algorithms to classify different punch types and detect errors. After optimizing the model and validating it with new data, the system achieved 89% accuracy in identifying punches, up from an initial 77% accuracy, by simplifying the user interface and removing noisy data for certain punch types.
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications
Ryan phillips week4_demo_noname
- 1. Building a Better Boxer with Wearable Technology Ryan Phillips
- 2. The Goal: Cleaner Woriout Feedbaci StraightPunches Power Punches Punches Jab Cross Uppercut Hook Block
- 3. Analyzing the Data RawData: • Motion inX,Y,Z • RotationinX, Y,Z Engineered Featuresin PostgreSQL: • Averages ofeach dimension • Absolute value of Y- motion Createddatabasein PostgreSQL EnteredFeatures intoRandom Forest Optimized using scikit-learn Validatedusing new data
- 4. How is this woriing? Not informative
- 5. How is this woriing? Not informative Informative!
- 6. Which Punches are being misclassified? Two challenges: - Jabs mixed with Crosses - Hooks often misclassified
- 7. Which Punches are being misclassified? Solutions: - Simplify UI; Collapse Jabs and Crosses into one category - Remove Noisy Hook Data Result: Accuracy increased from 77% to 89%
- 8. Thank you for Your Attention Ryan Phillips ryancphillips01@gmail.com