An Inertial Measurement Framework for Gesture Recognition and Applications
GW '01 Revised Papers from the International Gesture Workshop on Gesture and Sign Languages in Human-Computer Interaction
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Personal and Ubiquitous Computing - Memory and Sharing of Experiences
Gesture spotting with body-worn inertial sensors to detect user activities
Pattern Recognition
Wearable Activity Tracking in Car Manufacturing
IEEE Pervasive Computing
Dealing with sensor displacement in motion-based onbody activity recognition systems
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
A reconfigurable ferromagnetic input device
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Improving activity discovery with automatic neighborhood estimation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ISWC '09 Proceedings of the 2009 International Symposium on Wearable Computers
Creating new interfaces for musical expression: introduction to NIME
ACM SIGGRAPH 2009 Courses
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Proceedings of the 9th International Conference on Interaction Design and Children
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
FlexCon: robust context handling in human-oriented pervasive flows
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
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We investigate the use of magnetic field disturbances as features for motion based, wearable activity recognition systems. Such disturbances are mostly caused by large metallic objects and electrical appliances, both of which are often involved in human activities. We propose to detect them by subtracting angular velocity values computed from the changes in the magnetic field vector from gyroscope signals. We argue that for activities that are associated with specific objects or devices such features increase system robustness against motion variations, sensor displacement and inter user differences. On a previously published data set of 8 gym exercises we demonstrate that our approach can improve the recognition by up to 31% over gyroscope only and up to 17% over a combination of a gyroscope and 3D accelerometer. Improvements of 9.5% are also demonstrated for user independent training as well as for the case of displaced sensors. A particularly interesting result is the fact that adding the magnetic disturbance features significantly improves recognition based on the vector norm of accelerometers and gyroscopes. The norm is often used when the orientation of the sensor is not known. This is common when using a mobile phone or other consumer appliance as a sensor.