Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Out of the lab and into the woods: kinematic analysis in running using wearable sensors
Proceedings of the 13th international conference on Ubiquitous computing
StrokeTrack: wireless inertial motion tracking of human arms for stroke telerehabilitation
Proceedings of the First ACM Workshop on Mobile Systems, Applications, and Services for Healthcare
Enabling longitudinal assessment of ankle-foot orthosis efficacy for children with cerebral palsy
Proceedings of the 2nd Conference on Wireless Health
Proceedings of the Fifth International Conference on Body Area Networks
A BSN based service for post-surgical knee rehabilitation at home
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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Advances in the miniaturisation of inertial sensors have allowed the design of compact wireless inertial orientation trackers. Such devices require data fusion algorithms to process sensor data into estimated orientations. This paper examines the problem of inertial sensor data fusion and compares two alternative methods for orientation estimation: complementary filtering and Kalman filtering. Experiments are presented to assess the performance and accuracy of the resulting filters. The complementary filter structure is demonstrated to require up to nine times less execution time, while maintaining better accuracy across different movement scenarios, than the Kalman filter structure.