SleepGuard

Abstract

Sleep is an important part of our daily routine -we spend about one-third of our time doing it. By tracking the sleep-related events and activities, sleep monitoring provides the decision support to help us understand the sleep quality and the 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, but existing solutions do not take full advantage of the rich sensor data provided by these portable devices. In this paper, we develop a novel approach to track a wide range of sleep-related events using smartwatches. We show that it is possible to track, using a single smartwatch, sleep events like body postures and movements, acoustic events, and illumination conditions. From these events, a statistical model can be designed to effectively evaluate a user's sleep quality across various sleep stages. 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. We also show that SLEEPGUARD can help users to improve their sleep quality by helping them to understand causes of sleep problems.Recently, sleep monitoring based on off-the-shelf mobile and wearable devices has emerged as an alternative way to obtain information about one's sleeping patterns [62,68]. By taking advantage of diverse sensors, behaviours and routines associated with sleeping can be captured and modelled. This in turn can help users understand their sleep behaviour and provide feedback on how to improve their sleep, for example, by changing routines surrounding sleep activity or improving the sleeping environment. What makes self monitoring particularly attractive is the non-invasive nature of the sensing compared to PSG. Examples of consumer-grade sleep monitors range from apps running on smartphones or tablets to smartwatches and specialized wearable devices [10, 32,34, 58,67,69].Despite the popularity of consumer-grade sleep monitors, currently the full potential of these devices is not being realized. Indeed, while current consumer-grade sleep monitors can capture and model a wide range of sleep related information, such as estimating overall sleep quality, capturing different stages of sleep, and identifying specific events occurring during sleep [42,67,74], they offer little help in understanding the characteristics that surround poor sleep. Thus, these solutions are unable to capture the root cause behind poor sleep or to provide recommendations on how to improve sleep quality. This is because current solutions focus on monitoring characteristics of the sleep itself, without considering behaviours occurring during sleep and the environmental context affecting sleep, e.g., ambient light-level and noise. Indeed, sleep quality has been shown to depend on a wide range of factors. For example, inten...

Citizen neuroscience: Wearable technology and open software to study the human brain in its natural habitat

“…These sleep tracking systems include technologies that remotely measure physiological signals from the user, without direct body contact, such as smart home sensors (e.g., Google Nest hub 2) and undermattress sleep trackers (e.g., the Withings Sleep Analyzer). Additionally, many sleep-tracking systems are in the form of wearable or portable systems capable of collecting signals, such as actigraphy wristbands (e.g., Ancoli-Israel et al, 2003;Lichstein et al, 2006;Martin & Hakim, 2011;Morgenthaler et al, 2007;Sadeh, 2011), smartwatches (e.g., Alfeo et al, 2018;Chang et al, 2018;De Zambotti et al, 2018;Phan et al, 2015;Sun et al, 2017), smart rings (Altini & Kinnunen, 2021;Chaudhry et al, 2020;Malakhatka et al, 2021;Mehrabadi et al, 2020;Koskimäki et al, 2018), and EEG-based systems such as headbands (Arnal et al, 2020;Koushik et al, 2019;Mota-Rolim et al, 2019;Onton et al, 2016).…”

The Association Between Heart Rate Variability and Sleep Quality - a Narrative Review

“…Smartwatches have become increasingly popular in recent years 21 . People started recording all daily activities 22 , including sleep quality 23 , which is crucial in post-workout regeneration 24 . Presently, HRV is used not only by professional athletes to measure awareness for training 25 , but also by beginners 26 .…”

Citizen neuroscience: wearable technology and open software to study the human brain in its natural habitat

“…These sleep tracking systems include technologies that remotely measure physiological signals from the user, without direct body contact, such as smart home sensors (e.g., Google Nest hub 2) and undermattress sleep trackers (e.g., the Withings Sleep Analyzer). Additionally, many sleep-tracking systems are in the form of wearable or portable systems capable of collecting physiological signals, such as actigraphy wristbands (e.g., Ancoli-Israel et al, 2003;Lichstein et al, 2006;Martin et al, 2011;Morgenthaler et al, 2007;Sadeh, 2011), smart watches (e.g., Alfeo et al, 2018;Chang et al, 2018;De Zambotti et al, 2018;Phan et al, 2015;Sun et al, 2017), smart rings (Altini et al, 2021;Chaudhry et al, 2020;Malakhatka et al, 2021;Mehrabadi et al, 2020;Koskimäki et al, 2018), and EEG-based systems such as headbands (Arnal et al, 2020;Koushik et al, 2019;Mota-Rolim et al, 2019;Onton et al, 2016).…”