TY - GEN
T1 - Data Compression via Low Complexity Delta Transition Lossless Encoding for Remote Physiological and Environmental Monitoring
AU - Felton, Christopher L.
AU - Gilbert, Barry K.
AU - Haider, Clifton R.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Continuous remote physiologic and environmental monitoring, employing an ever-increasing array of sensors, is now commonplace. Given the significant amount of data being digitized, two common sources of energy consumption can be targeted to improve device runtime: data storage and data transmission. One embedded method to maximize device runtime is inline low energy data compression. Herein we present a low complexity data encoding scheme. We list and characterize the parameters necessary for encoding. The encoding method is then evaluated and tuned using contrived data with varying degrees of covariance, as well as open-source electrocardiography (ECG) data. Finally, the encoding method is evaluated with tri-axial accelerometry and ECG data previously collected on a Mount Everest Expedition using a remote physiologic monitor that was specifically designed for long autonomous runtimes. With the described low overhead delta transition lossless encoding method, the Mt. Everest device runtime would have doubled from two to four weeks of continuous recording. Finally, this approach would be beneficial given a requirement to transmit data wirelessly in real time, since the total transmission power and energy would be reduced by an amount related to the compression ratio.
AB - Continuous remote physiologic and environmental monitoring, employing an ever-increasing array of sensors, is now commonplace. Given the significant amount of data being digitized, two common sources of energy consumption can be targeted to improve device runtime: data storage and data transmission. One embedded method to maximize device runtime is inline low energy data compression. Herein we present a low complexity data encoding scheme. We list and characterize the parameters necessary for encoding. The encoding method is then evaluated and tuned using contrived data with varying degrees of covariance, as well as open-source electrocardiography (ECG) data. Finally, the encoding method is evaluated with tri-axial accelerometry and ECG data previously collected on a Mount Everest Expedition using a remote physiologic monitor that was specifically designed for long autonomous runtimes. With the described low overhead delta transition lossless encoding method, the Mt. Everest device runtime would have doubled from two to four weeks of continuous recording. Finally, this approach would be beneficial given a requirement to transmit data wirelessly in real time, since the total transmission power and energy would be reduced by an amount related to the compression ratio.
KW - Compression
KW - Electrocardiography
KW - Encoding
KW - Lossless
KW - Motion
KW - Physiological Monitors
KW - Wearables
UR - http://www.scopus.com/inward/record.url?scp=85056625262&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056625262&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8513277
DO - 10.1109/EMBC.2018.8513277
M3 - Conference contribution
C2 - 30441324
AN - SCOPUS:85056625262
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4379
EP - 4384
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -