Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
Robust periodicity detection algorithms
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Constrained ECG compression algorithm using the block-based discrete cosine transform
Digital Signal Processing
Non-intrusive Patient Monitoring of Alzheimer's Disease Subjects Using Wireless Sensor Networks
CONGRESS '09 Proceedings of the 2009 World Congress on Privacy, Security, Trust and the Management of e-Business
Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
GeM-REM: Generative Model-Driven Resource Efficient ECG Monitoring in Body Sensor Networks
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
Poly-DWT: Polymorphic wavelet hardware support for dynamic image compression
ACM Transactions on Embedded Computing Systems (TECS)
KNOWME: An Energy-Efficient Multimodal Body Area Network for Physical Activity Monitoring
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on CAPA'09, Special Section on WHS'09, and Special Section VCPSS' 09
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There is an increase rise in the usage of mobile health sensors in wearable devices and smartphones. These embedded systems have tight limits on storage, computation power, network connectivity and battery usage making it important to ensure efficient storage/ communication of sensor readings to centralized node/ server. Frequency Transform or Entropy encoding schemes such as arithmetic or Huffman coding can be used for compression, but they incur high computational cost in some scenarios or are oblivious to the higher level redundancies in signal. To this end, we used the property of periodicity in these naturally occurring signals such as heart rate or gait measurements to design a simple low cost scheme for data compression. First, a modified Chi-square periodogram metric is used to adaptively determine the exact time-varying periodicity of the signal. Next, the time-series signal is folded into Frames of length equal to a pre-determined period value. We have successfully tested the scheme for good compression performance in ECG, motion accelerometer data and Parkinson patients samples, leading to 8-14X compression in large sample sizes (6-8K samples) and 2-3X in small sample sizes (200 samples). The proposed scheme can be used stand-alone or as pre-processing step for existing techniques in literature.