IMUSim: A simulation environment for inertial sensing algorithm design and evaluation

TLDR: A simulation environment, specifically for inertial sensing applications, is presented, which simulates sensor readings based on continuous trajectory models, and shows how suitable models can be generated from existing motion capture or other sampled data.

Abstract: The use of wireless devices with accelerometers and gyroscopes to measure the movements of humans and objects is a growing area of interest. Applications range from simple activity detection to detailed full-body motion capture using networks of sensors worn on the body. A variety of algorithms have been proposed for these applications, but opportunities for accurate evaluation and comparison have been limited due to the many difficulties with performing rigorous experiments. We present a simulation environment, specifically for inertial sensing applications, designed to tackle this problem. We simulate sensor readings based on continuous trajectory models, and show how suitable models can be generated from existing motion capture or other sampled data. We show a good match between our simulated data and real sensor data for human movements. We also model a wide range of real-world issues such as non-ideal sensors, magnetic field distortions, timing factors and radio packet losses. To demonstrate the capabilities of our simulator, we present new results comparing four existing orientation estimation algorithms for human motion capture.