An Active Sleep Monitoring Framework Using Wearables

Abstract

Sleep is the most important aspect of healthy and active living. The right amount of sleep at the right time helps an individual to protect his or her physical, mental, and cognitive health and maintain his or her quality of life. The most durative of the Activities of Daily Living (ADL), sleep has a major synergic influence on a person's fuctional, behavioral, and cognitive health. A deep understanding of sleep behavior and its relationship with its physiological signals, and contexts (such as eye or body movements), is necessary to design and develop a robust intelligent sleep monitoring system. In this article, we propose an intelligent algorithm to detect the microscopic states of sleep that fundamentally constitute the components of good and bad sleeping behaviors and thus help shape the formative assessment of sleep quality. Our initial analysis includes the investigation of several classification techniques to identify and correlate the relationship of microscopic sleep states with overall sleep behavior. Subsequently, we also propose an online algorithm based on change point detection to process and classify the microscopic sleep states. We also develop a lightweight version of the proposed algorithm for real-time sleep monitoring, recognition, and assessment at scale. For a larger deployment of our proposed model across a community of individuals, we propose an active-learning-based methodology to reduce the effort of ground-truth data collection and labeling. Finally, we evaluate the performance of our proposed algorithms on real data traces and demonstrate the efficacy of our models for detecting and assessing the fine-grained sleep states beyond an individual.

Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape

“…The difference in accuracy between the top 2 [52], 87% and [32], 85.9% Is only 2.9% but their difference in N1 is 60.05%. Likewise the second highest N1 accuracy is [44] with 69.45% but with a significant drop in all other sleep stages. The two most successful approaches that achieve the highest N1% while not sacrificing the other sleep stages are [49,52] with N1 of 80.2% and 41.54% respectively.…”

“…as an example made subjects self report when they went to bed and when they were awake and used that as ground truth for awake/sleep classification. The way additional datasets were used also differed in the studies [44,42,43]. trained a model on their own data and tested against an additional dataset [45].…”

“…The presented attributes are; How many sleep stages were used; if the algorithm was based on ML or DL; if data was from one sensor or multiple modalities; what algorithm was used and what sensors were used. Some papers [47,51,27,39,44,34,32,42] tried multiple sleep stage combinations and therefore the best performing algorithm was chosen for each case. This causes the summary to add up to more than 27 and should instead be read as X out of 27 papers used this approach in their study.…”

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

“…They apply their model to support self-regulated learning and teaching support in educational environment. Hossain et al [50] propose a system to identify the microscopic sleep states which indicate good and bad sleeping behaviors in order to do formative assessment of sleep quality. They implement classification algorithms to identify and correlate microscopic sleep states and overall sleep behavior.…”

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

“…Sleep pose can be recognized by a contact-based approach [24]. Accelerometer [25], RFID [26], and pressure sensors [27] are used to acquire raw motion data of human, and inference sleep state from the motion data.…”