Stig Sensors provides a synchronized athlete-and-bike telemetry system for mountain bikers, combining wearable and bike-mounted sensor nodes with digital twin technology. The system captures rider biometrics, body positioning, suspension travel, and crash detection, then rebuilds rides in the cloud for performance analysis. Its platform delivers actionable coaching feedback by correlating human movement with bike dynamics in a unified dataset.
Funding
Funding not disclosed
Founders
Product
Problem
Mountain bikers lack access to integrated telemetry that captures both rider body mechanics and bike dynamics in a single synchronized dataset. Traditional cycling data systems focus on bike metrics alone, leaving riders without actionable insight into how their body positioning and movement affect performance, safety, and bike setup.
Solution
Stig Sensors provides a complete kinetic stack that combines athlete and bike metrics into one synchronized dataset. The system includes a wearable Athlete Node that tracks biometrics like heart rate, respiration, and VO2, alongside impact and crash detection. Bike-mounted Front Linkage and Back Triangle nodes measure steering, suspension travel, wheel speed, and frame orientation. All data is synchronized and reconstructed into a 1:1 digital twin of the ride in the cloud, allowing riders to visually analyze their body positioning relative to bike movements. The platform delivers coaching feedback through segment tracking, bike tuning optimization, and comparison against expert baselines to help riders improve form, prevent crashes, and shave time.
Target Audience
Primary users are mountain bikers ranging from recreational riders seeking safety features and foundational insights to competitive athletes and professionals who want to optimize body positioning, bike tuning, and performance margins.
Features
- Three-node hardware system: wearable Athlete Node, fork-mounted Front Linkage, and frame-mounted Back Triangle for full-body and bike telemetry
- Real-time biometric monitoring including VO2, respiration, and heart rate alongside impact and crash detection sensors
- Digital twin reconstruction in the cloud that rebuilds every jump, berm, and drop as a 1:1 visual model of rider and bike interaction
- Synchronized dataset correlating rider body position with front and rear suspension travel, steering angle, pedal cadence, and wheel speed
- GPS and segment tracking for trail navigation and performance benchmarking
- Coaching engine that identifies speed loss, form compromise, and crash causation by analyzing synchronized rider-bike data