Beacon-based sleep status and physical activity monitoring in humans.

Abstract

One out of every five people in Japan is dissatisfied with their sleep and various diseases caused by lack of exercise have been pointed out, but there are few effective remedies for these problems. In this study, we aimed to develop a simple method for measuring behavioral sleep patterns and physical activity using a beacon accelerometer wirelessly connected with a smartphone. A sleep prediction model was created comparing the data obtained from the accelerometer with the sleep status data obtained by a previously validated sleep monitoring system. The Random Forest model was able to classify sleep and wakefulness with a 97.4% and 85.4% precision, respectively, which were comparable to those of conventional acceleration-based sleep monitoring devices. Additionally, the same data acquisition method was used to classify exercise intensity into seven levels and a high correlation (r=0.813, p<0.0001) was found when comparing the classified exercise intensity to metabolic equivalent (MET) values. This suggests that the proposed method can be used for accurate measurement of both behavioral sleep and physical activity classifying over a long period of time.