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Dynamic Computation Offloading for Low-Power Wearable Health Monitoring Systems.
IEEE Transactions on Bio-medical Engineering 2017 March
OBJECTIVE: The objective of this paper is to describe and evaluate an algorithm to reduce power usage and increase battery lifetime for wearable health-monitoring devices.
METHODS: We describe a novel dynamic computation offloading scheme for real-time wearable health monitoring devices that adjusts the partitioning of data processing between the wearable device and mobile application as a function of desired classification accuracy.
RESULTS: By making the correct offloading decision based on current system parameters, we show that we are able to reduce system power by as much as 20%.
CONCLUSION: We demonstrate that computation offloading can be applied to real-time monitoring systems, and yields significant power savings.
SIGNIFICANCE: Making correct offloading decisions for health monitoring devices can extend battery life and improve adherence.
METHODS: We describe a novel dynamic computation offloading scheme for real-time wearable health monitoring devices that adjusts the partitioning of data processing between the wearable device and mobile application as a function of desired classification accuracy.
RESULTS: By making the correct offloading decision based on current system parameters, we show that we are able to reduce system power by as much as 20%.
CONCLUSION: We demonstrate that computation offloading can be applied to real-time monitoring systems, and yields significant power savings.
SIGNIFICANCE: Making correct offloading decisions for health monitoring devices can extend battery life and improve adherence.
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