Key result
IoT-connected deep learning for PPG signals predicts intensive exercise heart rate with ~5% error.
Why the study?
Does a deep learning technique using IoT cloud connectivity and accelerometer data accurately predict heart rate from PPG signals during intensive physical exercise?
Does a deep learning technique using IoT cloud connectivity and accelerometer data accurately predict heart rate from PPG signals during intensive physical exercise?
A novel deep learning approach combining PPG and accelerometer data via IoT cloud connectivity can accurately predict heart rate during intensive exercise with a low error margin.
Answers are grounded in this paper and cite their sources.
May facilitate real-time heart rate monitoring via wearables during exercise; leaves open prospective validation before clinical adoption.
Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However, current determination of heart rate through mobile applications suffers from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for selection of PPG signals using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.
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Vasu Jindal (2016) studied Heart rate monitoring during intensive physical exercise. Deep learning-based PPG signal selection using IoT cloud connectivity and accelerometer data was evaluated on Heart rate prediction error margin. A deep learning approach utilizing IoT cloud connectivity for PPG signal selection accurately predicted heart rate during intensive physical exercise with a 10-fold cross-validation error margin of 4.88%.
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