Key result
RespWatch, a wearable sensing system using a hybrid estimator, achieved the lowest overall mean absolute error for respiratory rate monitoring on smartwatches in a dataset of 30 participants.
Why the study?
Respiratory rate is a vital physiological signal for many health and clinical applications, motivating the development of robust smartwatch-based monitoring using photoplethysmography.
Does the RespWatch hybrid estimator improve respiratory rate monitoring accuracy compared to standalone signal processing or deep learning estimators in human participants?
Does the RespWatch hybrid estimator improve respiratory rate monitoring accuracy compared to standalone signal processing or deep learning estimators in human participants?
RespWatch demonstrates the feasibility and efficiency of using a hybrid signal processing and deep learning approach for robust respiratory rate monitoring on commercial smartwatches.
May support hybrid estimators for wearable respiratory rate monitoring; leaves open validation in larger clinical cohorts.
Respiratory rate (RR) is a physiological signal that is vital for many health and clinical applications. This paper presents RespWatch, a wearable sensing system for robust RR monitoring on smartwatches with Photoplethysmography (PPG). We designed two novel RR estimators based on signal processing and deep learning. The signal processing estimator achieved high accuracy and efficiency in the presence of moderate noise. In comparison, the deep learning estimator, based on a convolutional neural network (CNN), was more robust against noise artifacts at a higher processing cost. To exploit their complementary strengths, we further developed a hybrid estimator that dynamically switches between the signal processing and deep learning estimators based on a new Estimation Quality Index (EQI). We evaluated and compared these approaches on a dataset collected from 30 participants. The hybrid estimator achieved the lowest overall mean absolute error, balancing robustness and efficiency. Furthermore, we implemented RespWatch on commercial Wear OS smartwatches. Empirical evaluation demonstrated the feasibility and efficiency of RespWatch for RR monitoring on smartwatch platforms.
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Dai et al. (2021) studied Respiratory rate monitoring (n=30). RespWatch (hybrid estimator) vs. Signal processing and deep learning estimators was evaluated on Mean absolute error. RespWatch, a wearable sensing system using a hybrid estimator, achieved the lowest overall mean absolute error for respiratory rate monitoring on smartwatches in a dataset of 30 participants.
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