Key result
The A-HeaRing adaptive HR sampling algorithm reduced smartwatch battery power consumption by more than 50% during running events while maintaining a minimum of 90% accuracy in HR zone estimation.
Why the study?
Continuous heart rate monitoring during high acceleration events like running consumes substantial battery power in smart watches, creating a need to reduce sampling without missing key heart rate information.
An adaptive HR sampling algorithm can reduce smartwatch battery consumption by over 50% during running while maintaining at least 90% accuracy in HR zone estimation.
Heart Rate (HR) monitoring in a smart watch is a battery consuming process. Therefore, some smart watches prevent continuous HR sampling during low-level physical activities for improving the power usage of batteries. However, high acceleration events like running require continuous HR sampling for recording the varying HR readings, which consumes a lot of battery power. Therefore, the challenge is to reduce the sampling during running while recording an individual’s HR variation (HRV). Our approach prevents continuous HR sampling for long-running events without missing important HR information. In this context, we analyze the existing real-life HRs and acceleration datasets recorded on humans during long runs. We design an adaptive HR sampling algorithm, A-HeaRing, from the extracted HR variation details. A-HeaRing reduces the battery power consumption of a smart watch by more than 50% while recording a running event. Additionally, we design a data regeneration system for missing HR readings. The regeneration system provides a minimum of 90% accuracy in the HR zone estimation.
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Vyas et al. (2023) studied Heart rate monitoring during running. A-HeaRing adaptive HR sampling algorithm vs. Continuous HR sampling was evaluated on Battery power consumption and HR zone estimation accuracy. The A-HeaRing adaptive HR sampling algorithm reduced smartwatch battery power consumption by more than 50% during running events while maintaining a minimum of 90% accuracy in HR zone estimation.
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