Unobtrusive detection of body movements during sleep using Wi-Fi received signal strength with model adaptation technique

Although sleep is essential to healthy living, many people have issues related to insufficient or poor quality sleep. In this study, we propose a method for unobtrusively detecting body movements during sleep by measuring changes in Wi-Fi signal strength between two Wi-Fi-enabled devices because prior research has found a correlation between sleep state and body movements such as rolling over. In our method, users place two Wi-Fi-enabled devices, on the left and right sides of their bed when sleeping. Our method then detects body movement by measuring changes in Wi-Fi signal strength between the two devices. By doing so, our method can detect motion without any sensors connected to the user, using devices that are equipped with commercially available Wi-Fi modules. The main feature of our method is its ability to train a user's body movement detection model on other users' training data. We employ a model adaptation technique called the maximum likelihood linear regression (MLLR) to adapt a user-independent movement detection model to the user of interest. In this study, we evaluated our method using 60 sessions of data collected from six participants, and achieved approximately 82% accuracy on average with user-independent movement detection models.