Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Designing Ubiquitous Computing Systems for Sports Equipment
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
Memorizing What You Did Last Week: Towards Detailed Actigraphy With A Wearable Sensor
ICDCSW '07 Proceedings of the 27th International Conference on Distributed Computing Systems Workshops
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
SIAM Journal on Imaging Sciences
SenseCam: a retrospective memory aid
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices
Pervasive and Mobile Computing
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In selected scenarios, sensor data capturing with mobile devices can be separated from the data processing step. In these cases, Compressive Sensing allows a significant reduction of the average sampling rate below the Nyquist rate, if the signal has a sparse frequency representation. This can be motivated in order to increase the energy efficiency of the mobile device and extend its runtime. Since many signals, especially in the field of motion recognition, are time-dependent, we propose a corresponding general sampling algorithm for time-dependent signals. It even allows a declining average sampling rate if the data acquisition is extended beyond a projected acquisition end. The presented approach is testified for the purpose of motion recognition by evaluating real acceleration sensor data acquired with the proposed algorithm.