SleepMonitor

Abstract

Respiratory rate and body position are two major physiological parameters in sleep study, and monitoring them during sleep can provide helpful information for health care. In this paper, we present SleepMonitor, a smartwatch based system which leverages the built-in accelerometer to monitor the respiratory rate and body position. To calculate respiratory rate, we design a filter to extract the weak respiratory signal from the noisy accelerometer data collected on the wrist, and use frequency analysis to estimate the respiratory rate from the data along each axis. Further, we design a multi-axis fusion approach which can adaptively adjust the estimates from the three axes and then significantly improve the estimation accuracy. To detect the body position, we apply machine learning techniques based on the features extracted from the accelerometer data. We have implemented our system on Android Wear based smartwatches and evaluated its performance in real experiments. The results show that our system can monitor respiratory rate and body position during sleep with high accuracy under various conditions.