TY - JOUR

T1 - SleepGuard

T2 - capturing rich sleep information using smartwatch sensing data

AU - Chang, Liqiong

AU - Lu, Jiaqi

AU - Wang, Ju

AU - Chen, Xiaojiang

AU - Fang, Dingyi

AU - Tang, Zhanyong

AU - Nurmi, Petteri Tapio

AU - Wang, Zheng

N1 - © ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2 Issue 3, September 2018 http://doi.acm.org/10.1145/3264908

PY - 2018/9

Y1 - 2018/9

N2 - Sleep is an important part of our daily routine – we spend about one-third of our time doing it. By tracking sleep-related events and activities, sleep monitoring provides decision support to help us understand sleep quality and causes of poor sleep. Wearable devices provide a new way for sleep monitoring, allowing us to monitor sleep from the comfort of our own home. However, existing solutions do not take full advantage of the rich sensor data provided by these devices. In this paper, we present the design and development of SleepGuard, a novel approach to track a wide range of sleep-related events using smartwatches. We show that using merely a single smartwatch,it is possible to capture a rich amount of information about sleep events and sleeping context, including body posture and movements, acoustic events, and illumination conditions. We demonstrate that through these events it is possible to estimate sleep quality and identify factors affecting it most. We evaluate our approach by conducting extensive experiments involved fifteen users across a 2-week period. Our experimental results show that our approach can track a richer set of sleep events, provide better decision support for evaluating sleep quality, and help to identify causes for sleep problems compared to prior work.

AB - Sleep is an important part of our daily routine – we spend about one-third of our time doing it. By tracking sleep-related events and activities, sleep monitoring provides decision support to help us understand sleep quality and causes of poor sleep. Wearable devices provide a new way for sleep monitoring, allowing us to monitor sleep from the comfort of our own home. However, existing solutions do not take full advantage of the rich sensor data provided by these devices. In this paper, we present the design and development of SleepGuard, a novel approach to track a wide range of sleep-related events using smartwatches. We show that using merely a single smartwatch,it is possible to capture a rich amount of information about sleep events and sleeping context, including body posture and movements, acoustic events, and illumination conditions. We demonstrate that through these events it is possible to estimate sleep quality and identify factors affecting it most. We evaluate our approach by conducting extensive experiments involved fifteen users across a 2-week period. Our experimental results show that our approach can track a richer set of sleep events, provide better decision support for evaluating sleep quality, and help to identify causes for sleep problems compared to prior work.

U2 - 10.1145/3264908

DO - 10.1145/3264908

M3 - Journal article

VL - 2

JO - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

JF - Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

SN - 2474-9567

IS - 3

M1 - 98

ER -