MotionStack tackles tough technical challenges.

Normalization and Standardization — All data is reported in specified units across all device configurations.

Bias Correction — Removes unwanted offset in the data from the actual value.

Sensor Fusion — Couples multiple different sensors to increase accuracy and precision.

Adaptive Filters — Optimized filter parameters respond to changing signal characteristics.

Signal Coarsening — Compresses motion data in real-time to only process, transfer, and store vital information.

Sensor Polyfill — Fills in missing sensors by transforming available sensors.

Machine Learning — Optimizes the system response to best match the expected response. (non-linear optimization)

Genetic Algorithms — Intelligently searches for globally optimal solution using randomized population evolution scheme.

Neural Networks — Intelligently searches for a globally optimal solution using biological inspired networks.

Minimal Dependencies — Self contained and built of standard web technologies for immense scalability.

Massive Collection of Recorded Motion Data

MotionStack is a radically new way to design and build motion and spatial computing experiences: https://motionstack.adtile.me