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.
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.