SleePS: Sleep position tracking system for screening sleep quality by wristbands

Sleep plays an important role in recovering physical and mental functions. Sleep position is known to affect sleep quality, hence, managing sleep position is beneficial for patients suffering from sleep disorders. For a long-term sleep management, we propose a sleep position tracking system using two wristbands. From the data collected from the wristbands, the system detects sleep positions and their changes. We define a sleep position motion model that consists of seven transitions between three sleep positions. Then, we propose pre-processing methods to overcome difficulties in analyzing sleep motion data, i.e., discontinuity, uncertainty, and time-variability. We tested experimental data in state-of-art pre-trained convolution neural networks by transfer learning. The accuracy of our proposed system was 96.03% and 88.02% in pilot experiment and on-site sleep experiment, respectively. Our experimental results demonstrate that the proposed system effectively and accurately keeps track of sleep positions without causing any inconvenience to users, and hence, serves as a key building block for cost-effective 24/7 sleep monitoring solutions.