Key result
A PPG-based wearable device using a Centralized State Sensing algorithm improved heart rate estimation mean absolute error by 26.8% and achieved 84.29% accuracy for blood glucose estimation.
Why the study?
Individualized health-care has gained traction due to advances in sensor technology, edge computing, and communication technologies, motivating wearable devices for mobile health-care using PPG signals.
Does a wearable device with PPG sensors and Centralized State Sensing algorithm improve the estimation of heart rate and blood glucose?
Does a wearable device with PPG sensors and Centralized State Sensing algorithm improve the estimation of heart rate and blood glucose?
A novel PPG-based wearable device with advanced algorithms demonstrates improved accuracy for heart rate and non-invasive blood glucose estimation.
May enable PPG-based mHealth wearables; leaves open prospective clinical validation before adoption.
Individualized health-care is gaining traction recently due to the advances in sensor technology, edge computing and the improvement in communication technologies. In this work, we show the application of wearable device for mobile health-care along with supporting algorithms using photoplethysmogram (PPG) as our signal source. We introduce the design of our wearable device equipped PPG sensors for extracting valuable bio-information from the human body. Using our device, we introduce Centralized State Sensing (CSS), an algorithm that improves the estimation of different health parameters. By using heart rate estimation as an example, the proposed algorithm is 26.8% better in terms of mean absolute error when compared with the average reading taken across sensors. We also propose a deep neural network model applied to a PPG-glucose dataset containing 349 samples provided by our industry partner for non-invasive blood glucose estimation achieving an accuracy of 84.29% based on ISO:15197:2013 standard.
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Lee et al. (2021) studied this question. PPG-based wearable device with Centralized State Sensing (CSS) algorithm and deep neural network vs. Average reading taken across sensors was evaluated on Heart rate estimation mean absolute error and blood glucose estimation accuracy. A PPG-based wearable device using a Centralized State Sensing algorithm improved heart rate estimation mean absolute error by 26.8% and achieved 84.29% accuracy for blood glucose estimation.
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