Data Compression via Low Complexity Delta Transition Lossless Encoding for Remote Physiological and Environmental Monitoring.

Abstract

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.