Machine learning based quantitative analysis of human sleep
Principal Investigator: Dr Shoi Shi
Approved Research ID: 48357
Approval date: July 3rd 2019
Sleep deficiency is a common public health problem in many countries, such as the United States, U.K., or Japan. If we know how long people should sleep, we can diagnose sleep deficiency quantitatively. However, we still do not know this. We have already developed a machine learning based algorithm which enable us to detect sleep in high accuracy from the activity data. By applying this algorithm to the 100,00 activity data stored in the U.K. BioBank, we can obtain the largest data set of sleep in the world and the highly accurate distribution of sleep length. We plan to use the first 6 months for adjusting our algorithm to the data obtained by the AX3 sensor which is used in the U.K. BioBank. With the later 6 months, we will analyze all data (100,000 subjects) and prepare for publication.