Motion Tracking: No Silver Bullet, but a Respectable Arsenal
IEEE Computer Graphics and Applications
Effect of Latency on Presence in Stressful Virtual Environments
VR '03 Proceedings of the IEEE Virtual Reality 2003
Inertial Head-Tracker Sensor Fusion by a Complimentary Separate-Bias Kalman Filter
VRAIS '96 Proceedings of the 1996 Virtual Reality Annual International Symposium (VRAIS 96)
APGV '04 Proceedings of the 1st Symposium on Applied perception in graphics and visualization
A Compact, Wireless, Wearable Sensor Network for Interactive Dance Ensembles
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Problems and Prospects for Intimate Musical Control of Computers
Computer Music Journal
Orient-2: a realtime wireless posture tracking system using local orientation estimation
Proceedings of the 4th workshop on Embedded networked sensors
Motion tracking algorithms for inertial measurement
Proceedings of the ICST 2nd international conference on Body area networks
Comparison of Orientation Filter Algorithms for Realtime Wireless Inertial Posture Tracking
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Minimising Loss-Induced Errors in Real Time Wireless Sensing by Avoiding Data Dependency
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
IEEE Transactions on Robotics
On the use of magnetic field disturbances as features for activity recognition with on body sensors
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
From posture to motion: the challenge for real time wireless inertial motion capture
Proceedings of the Fifth International Conference on Body Area Networks
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Motion capture using wireless inertial measurement units (IMUs) has many advantages over other techniques. Achieving accurate tracking with IMUs presents a processing challenge, especially for real time tracking. Centralised approaches are bandwidth-intensive and prone to error from packet loss. Methods based solely on local knowledge have poor dynamic accuracy, due to ambiguities introduced by linear acceleration. First we analyse the effect of linear acceleration on orientation accuracy. We then present an efficient distributed method which uses a model of the subject's body structure to estimate and correct for linear acceleration. We validate the behaviour of this method on data from combined optical/inertial capture experiments, and show improved gravity vector estimation and a corresponding increase in orientation accuracy. We estimate the runtime, memory, communication and power requirements of our method, and show that it is a practical software modification to an existing system. The proposed solution is the first to use collaboration between wireless IMUs to improve accuracy.