Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone (original) (raw)
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Recognizing Sleep Stages with Wearable Sensors in Everyday Settings
Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, 2017
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A New Advanced Implementation Results of Comprehensive Statistical Analysis for Recent Smartphone Sleep-Tracking Applications, 2023
Background: Sleep disorders and lack of sleep are serious health problems that must be accurately monitored and closely followed-up before the cumulative long-term effects of sleep deprivation and sleep disorders have been associated with a wide range of deleterious health consequences including an increased risk of hypertension, diabetes, obesity, depression, heart attack, and stroke. Therefore, in these recent years of thriving technologies especially in the fields of Smartphones, biometric sensors, Artificial Intelligence, Programming and App developing; leading to thousands of downloadable apps from different stores (Google play, and Apple store) that offer a stable, controllable, and scalable options for sleep tracking apps utilization at general population level since it became an important target of health and fitness app developers. Moreover, the recent sleep tracking apps have many features and functionalities including smart alarms, sleep aids, sleep cycles, and sleep analysis. Objective: This study aims to comprehensively survey, review and analyze most recent smartphones' sleep tracking apps in the period from Februaryto-June 2023 scouting and exploring their technical features and functionalities in order to comprehensively explore their effectiveness in sleep tracking activity of users. Methods: This study has surveyed the Apple Store and Google Play as the two major stores for most popular sleep tracking apps. The following keywords were used in our search for applications: sleep apps, sleep monitoring, sleep tracking, sleep analysis, sleep quality, sleep coaching, and sleep statistics. Titles, descriptions, and keywords of the selected applications were thoroughly checked and reviewed. First all publicly available smartphones' apps in stores were included. Then, apps used for other purposes than sleep tracking were excluded. Moreover, apps that are not intended for self-management sleep tracking such as baby sleep tracking apps were excluded during the apps screening and filtering process. Furthermore, the apps that have the same technical features and/or functionalities were excluded for duplication, along with the low usage and low ratings apps during the apps eligibility filtering process. Finally, the remaining apps were rated individually for consistency with the well-known American Psychiatric Association (APA) app evaluation model taken to consideration that each store was analyzed separately. Results: a total of 157 most recent sleep-tracking related apps were included at first in our study which took place during the period from Februaryto-end of June 2023 focusing on 24 major technical features of sleep-tracking apps, 32 apps (20.38%) were excluded from the study due to irrelevant main design and implementation purposes, while another 9 apps (5.73%) were excluded for not supporting self-management sleep tracking, then another 12 apps (7.64%) were excluded due to low usage and low rating during study period. Moreover, 21 apps (13.37%) did not meet the designed requirement values of APA-based statistical model of the study, finally 83 apps (52.86%) satisfied all the applications inclusion criteria, technical features of the study, and designed statistical model values. Conclusions: Even though there are several hundreds of smartphones' sleep-tracking applications that were approved, this statistical and analytical study evaluates and examines the 24 content analysis and major technical features in 83 of the more recent with users' most highly rated apps in Apple and Google play stores with several advantages and beneficial features, including smart alarms and sleep aids. However, a review of apps that met the inclusion requirements revealed that sleep-tracking applications can utilize more improvements in technical features design and implementation, along with privacy for users' data sharing.
The Science of Sweet Dreams: Predicting Sleep Efficiency from Wearable Device Data
Sleep is an important human behavior with a deep impact on quality of life. Inadequate sleep quality negatively affects both mental and physical well-being, and exacerbates many health problems such as diabetes, depression, cancer and obesity. Alarmingly, poor sleep is becoming a growing concern in our society. Increased efforts toward the development of sleep science, focus on the study of sleep and its impact on overall human health. Technology has become a crucial component for this research, particularly the use of wearable devices for capturing and analysing human activity. In this article, we explore the use of wearables in sleep science, including the algorithmic development of robust human activity recognition and predictive methodologies for sleep quality estimation, which have shown an AUC improvement up to twofold over current actigraphy methods by 50%. These technologies are crucial for the evolution of new application services to assist behavioural health decision-making for patients and professionals.
Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition
IEEE Access
Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and overfitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: ''https://github.com/zhaoaite/SSRM ''.
Already up? using mobile phones to track & share sleep behavior
International Journal of Human-Computer Studies, 2013
Users share a lot of personal information with friends, family members, and colleagues via social networks. Surprisingly, some users choose to share their sleeping patterns, perhaps both for awareness as well as a sense of connection to others. Indeed, sharing basic sleep data, whether a person has gone to bed or waking up, informs others about not just one's sleeping routines but also indicates physical state, and reflects a sense of wellness. We present Somnometer, a social alarm clock for mobile phones that helps users to capture and share their sleep patterns. While the sleep rating is obtained from explicit user input, the sleep duration is estimated based on monitoring a user's interactions with the app. Observing that many individuals currently utilize their mobile phone as an alarm clock revealed behavioral patterns that we were able to leverage when designing the app. We assess whether it is possible to reliably monitor one's sleep duration using such apps. We further investigate whether providing users with the ability to track their sleep behavior over a long time period can empower them to engage in healthier sleep habits. We hypothesize that sharing sleep information with social networks impacts awareness and connectedness among friends. The result from a controlled study reveals that it is feasible to monitor a user's sleep duration based just on her interactions with an alarm clock app on the mobile phone. The results from both an in-the-wild study and a controlled experiment suggest that providing a way for users to track their sleep behaviors increased user awareness of sleep patterns and induced healthier habits. However, we also found that, given the current broadcast nature of existing social networks, users were concerned with sharing their sleep patterns indiscriminately.