Empowering Wearable Sensor Generated Data to Predict Changes in Individual's Sleep Quality

Abstract

Wearable sensors found in popular wrist wearable device are both generating sales profit and constantly generating vast amount of data. Some of these wearable sensors are able to record physical activity and sleep trends, both are being mainly used to give insight to its users about their current and past health and well-being. We proposed a method of data preprocessing and machine learning using simple k-nearest neighbor classifier to furthermore empower the usage of such data to predict changes in one's sleep quality based on his or her current physical activity level. Our method were challenged to predict changes in five medically-approved sleep quality indicators, using data generated by commercially available consumer-grade wrist wearable device. The experiment result shows that the successful prediction of changes in sleep quality using wearable sensor generated data can be achieved by successfully selecting and sometimes combining the right input parameter(s). Each sleep quality indicators calls for different input parameter or combined parameters. By selecting and combining the right parameter(s), our method had successfully predict changes in both sleep duration and sleep efficiency with accuracy of 68% and 64%, respectively.