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Improved Sleep Detection Through the Fusion of Phone Agent and Wearable Data Streams

Commercial grade activity trackers and phone agents are increasingly being deployed as sensors for sleep in large scale, longitudinal designs. In general, wearables detect sleep through diminished movement and decreased heart rate (HR), while phone agents look for lack of user input, movement, sound or light. However, recent literature suggests that commercial-grade wearables and phone apps vary greatly in the accuracy of sleep predictions. Constant innovation in wearables and proprietary algorithms further make it difficult to evaluate their efficacy for scientific study, especially outside of the laboratory. In a longitudinal study, we find that wearables cannot detect when a person is laying still but using their phones, a common behavior, overestimating sleep when compared to self-reports. Therefore, we propose that fusing wearables and phone sensors allows for more accurate sleep detection by capitalizing on the benefits of both streams: combining the movement detection of wearables with the technology usage detected by cell phones. We determine that fusing phone activity to wearables can generate better models of self-reported sleep than either stream alone, and test models in two separate datasets.