Minimising Errors

About

Background: The system is based on creating a predictive model of the motion and this is dynamically optimised by applying sensed motion data to generate optimised system parameters. This method, unlike traditional methods of integration of acceleration and rotational velocities, does not compound errors which results in the predicted position of the sensor to drift from its true value. The system and method can be applied to determine the motion of mechanical, electro-mechanical and or biomechanical systems including human joint and or limb movement. The Challenge:  Inertial motion capture systems are becoming increasingly prominent in today’s market. They are often an attractive alternative to optically-based systems, which are expensive, susceptible to marker occlusion, and have limited capture areas. However, current inertia-based motion capture systems are often bulky and inaccurate. Accuracy issues arise, particularly when calculating displacement. Inertial measurement units (IMU) typically output linear acceleration and rotational velocities which, when integrated, can provide estimates of linear displacement.  The Solution: We have overcome the orientation aspect of this problem by using a dynamic model (based on Lagrangian mechanics) of the system to constrain the drift due to integration. The model is used to interpret and constrain the sensor measurements and will lead to a motion capture system whose accuracy is comparable to optical systems at a fraction of the cost.