Advancing Athletic Intelligence (AI)
through thorough research.
At Flux Labs, we're pioneering the integration of biomechanical engineering, adaptive AI, and wearable technology to revolutionize human performance.
Build-First Methodology
The why behind why we build first.
Traditional research often gets stuck in theoretical models. We take a different approach: building functional prototypes first, then refining through athlete testing and machine learning optimization.
Rapid Prototyping
Functional hardware within weeks, not years
Real-World Testing
Data collection with competitive athletes
ML Optimization
Algorithms trained on actual performance data
Current Focus: v0.1.0 System
Our first complete system integrates custom hardware with adaptive AI to provide real-time vocal coaching for runners:
Hybrid LSTM-Transformer model processing 42 biomechanical inputs
Custom IMU array + low-latency physiological sensors
~15% improvement in results from early trials
Our Research Mission
Flux Labs was founded on a simple premise: current wearable technology fails to actively enhance athletic performance in real-time. While sensors collect vast amounts of data, the critical leap from measurement to meaningful intervention remains unexplored.
Our research bridges this gap through a build-first approach. We prototype rapidly, test relentlessly, and deploy innovations that actually improve athlete outcomes.
Core Objective
Develop AI systems that don't just track, but actively optimize athletic performance through real-time biomechanical and physiological adaptation.
Methodology
Rapid prototyping → athlete testing → machine learning optimization → hardware refinement. Repeat.
Our research pillars:
Biomechanical Optimization
Our proprietary motion capture system analyzes 42 kinematic variables in real-time to model running efficiency. The system correlates these with metabolic cost to identify optimal movement patterns for individual athletes.
Learn moreAdaptive AI Architecture
We've developed a hybrid neural network that combines LSTM layers for temporal analysis with transformer-based attention mechanisms for physiological pattern recognition, achieving 92% accuracy in predicting athlete fatigue onset.
Learn moreMechatronic Systems
Custom-designed sensor arrays and processing units that provide lab-grade physiological monitoring in wearable form factors. Our hardware achieves 20ms latency from sensor input to AI processing.
Learn moreResearch Team
Collaborators
Our work is conducted in collaboration with (ADD THIS HERE), with additional support from the Center for Engineering Innovation and Design (CEID) and (MAYBE TSAI??).
Publications (all placeholders for now!)
Real-Time Biomechanical Feedback Systems for Endurance Athletes
Journal of Sports Engineering and Technology (2024)
Low-Latency Edge AI for Wearable Athletic Cognition Systems
IEEE Transactions on Human-Machine Systems (2024)
Metabolic Cost Prediction from Wearable Sensor Data
Nature Sports Science (2024)
Interested? Join us.
We're always looking for collaborators, test subjects, research partners, and investors. Whether you're an athlete, engineer, researcher, or investor, there's a place for you in our work.
Contact us (we respond!)