Key result
A post-calibration machine learning model using rolling windows significantly reduced the mean absolute error of heart rate estimation from a consumer wrist-worn device by 18.73% across all activity states.
Why the study?
Motion artifacts frequently occur during daily activities when using wrist-worn photoplethysmography sensors, compromising heart rate estimation accuracy.
Does a post-calibration approach using rolling windows improve heart rate estimation accuracy on consumer-grade wrist-worn devices compared to uncalibrated measurements?
Population
29 participants across three activity states
Comparison
Four wearable devices vs standard ECG measurement
Follow-up
130 minutes/person
Authors
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May improve wearable HR accuracy; leaves open clinical utility pending prospective validation.
Does a post-calibration approach using rolling windows improve heart rate estimation accuracy on consumer-grade wrist-worn devices compared to uncalibrated measurements?
Effect estimate: 18.73% reduction
A post-calibration approach using rolling windows significantly improves the accuracy of heart rate estimation from consumer-grade wrist-worn devices across various activity states.
Choksatchawathi et al. (2020) studied Healthy (n=29). Post-calibration machine learning model (rolling regression) vs. Uncalibrated Fitbit Charge HR data was evaluated on Mean absolute error (MAE) of heart rate estimation across all activity states (18.73% reduction). A post-calibration machine learning model using rolling windows significantly reduced the mean absolute error of heart rate estimation from a consumer wrist-worn device by 18.73% across all activity states.
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