Can Sequence Mining Improve Your Morning Mood? Toward a Precise Non-invasive Smart Clock (original) (raw)
Related papers
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
The paper presents results from the SmartSleep project which aims at developing a smartphone app that gives users individual advice on how to change their behaviour to improve their sleep. The advice is generated by identifying correlations between behaviour during the day and sleep architecture. To this end, the project addresses two sub-tasks: detecting a user's daytime behaviour and recognising sleep stages in an everyday setting. The focus of the paper is on the second task. Various sensor devices from the consumer market were used in addition to the usual PSG sensors in a sleep lab. An expert assigned a sleep stage for every 30 seconds. Subsequently, a sleep stage classifier was learned from the resulting sensor data streams segmented into labelled sleep stages of 30 seconds each. Apart from handcrafted features we also experimented with unsupervised feature learning based on the deep learning paradigm. Our best results for correctly classified sleep stages are in the range of 90 to 91% for Wake, REM and N3, while the best recognition rate for N2 is 83%. The classification results for N1 turned out to be much worse, N1 being mostly confused with N2.
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.
Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos and Posters, 2019
Good sleep is a key component of good health, and as such, how to obtain quality sleep is of concern to many people. Circadian rhythms vary between individuals and play an important role in regulating sleep, however, they are currently not monitored by commercially available wearables. Previous work has shown that circadian rhythm is reflected in changes of wrist temperature. In this work, we present a prototype wristband that measures motion and temperature at the wrist. We developed an algorithm to detect wrist temperature increase onset, which is an indicator of the body preparing for sleep. Our results demonstrate that our algorithm is able to detect wrist temperature increase onset, which appears to occur at the same time for the same person. We also show that temperature increase onset varies between people as does overall temperature patterns between people. The detection of wrist temperature patterns gives us a deeper understanding of the mechanisms underlying sleep and could be a valuable component of a personalized sleep monitoring algorithm.
An smartphone-based algorithm to measure and model quantity of sleep
2015
Sleep quantity affects an individual's personal health. The gold standard of measuring sleep and diagnosing sleep disorders is Polysomnography (PSG). Although PSG is accurate, it is expensive and it lacks portability. A number of wearable devices with embedded sensors have emerged in the recent past as an alternative to PSG for regular sleep monitoring directly by the user. These devices are intrusive and cause discomfort besides being expensive. In this work, we present an algorithm to detect sleep using a smartphone with the help of its inbuilt accelerometer sensor. We present three different approaches to classify raw acceleration data into two states - Sleep and Wake. In the first approach, we take an equation from Kushida's algorithm to process accelerometer data. Henceforth, we call it Kushida's equation. While the second is based on statistical functions, the third is based on Hidden Markov Model (HMM) training. Although all the three approaches are suitable for a...
Alarm clock using sleep analysis
International Journal of Biomedical Engineering and Technology, 2011
This study is to implement an alarm clock that sounds based on the sleep state of the user. Using known scientific knowledge of the human sleep cycle and its characteristic brain activity, the clock sounds its alarm when the user is in an easily aroused state within a time period set by the user. The user will feel more refreshed than, if they were awakened by a conventional alarm clock (George and Humberto, 2001). There is also a function which helps in recording the hypnogram. The logical processing of brain signals was achieved through National Instruments' LABVIEW.
Detecting sleep outside the clinic using wearable heart rate devices
Scientific Reports
The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can sidestep laborious human annotations and objectify cross-cohort comparisons. We developed and tested a heart rate-based algorithm that captures inter- and intra-individual sleep differences in free-living conditions and does not require human input. We evaluated it on four study cohorts using different research- and consumer-grade devices for over 2000 nights. Recording periods included both 24 h free-living and conventional lab-based night-only data. We compared our optimized method against polysomnography, sleep diaries and sleep periods produced through a state-of-the-art acceleration based method. Against sleep diaries, the algorithm yielded a mean squared error of 0.04–0.06 and a total sleep time (TST) deviation of -$$ - 2.70 (± 5.74) and 12.80 (± 3.89) minutes, respectively. When evaluated with PSG lab studie...
As the modern society accumulates sleep debt that jeopardizes health, performance and wellbeing, people become increasingly interested in self-assessment. We aim to enable sleep self-evaluation using available Heart Rate (HR) monitors, mobile and cloud technology. Sleep was evaluated using a proprietary ECG-based validated sleep diagnostic software adapted to HR data obtained from HR monitor belts (HRMs) which are widely used to monitor HR during physical activity. Data were transmitted and stored on an iPhone, using a dedicated application. Two wireless communication channels are used for HRMs: (P1) Wearlink and (P2) ANT+. The stored information was uploaded to the cloud and automatically analyzed. The functionality of an automated sleep monitoring and analysis, using HRMs, with either P1 or P2 transmission, iPhone, and cloud based SleepRate software analysis has been checked. HR belts with ANT+ were the most suitable for recording HR during sleep. Millions of people own HRMs to as...
Improved Sleep Detection Through the Fusion of Phone Agent and Wearable Data Streams
2020
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 wear...
CICLOGRAMA: A TOOL FOR DETECTION OF RHYTHMICITIES IN SLEEP/WAKE CYCLES
Chronobiology International, 2002
The Fourier spectral analysis of binary time series (or rectangular signals) causes methodological problems, due to the fact that it is based on sinusoidal functions. We propose a new tool for the detection of periodicities in binary time series, focusing on sleep/wake cycles. This methodology is based on a weighted histogram of cycle durations. In this paper, we compare our methodology with the Fourier spectral analysis on the basis of simulated and real binary data sets of various lengths. We also provide an approach to statistical validation of the periodicities determined with our methodology. Furthermore, we analyze the discriminating power of both methods in terms of standard deviation. Our results indicate that the Ciclograma is much more powerful than Fourier analysis when applied on this type of time series.
Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone
Journal of Communications Software and Systems, 2018
It is widely recognized that sleep is a basic physiological process having fundamental effects on human health, performance and well-being. Such evidence stimulates the research of solutions to foster self-awareness of personal sleeping habits, and correct living environment management policies to encourage sleep. In this context, the use of mobile technologies powered with automatic sleep recognition capabilities can be helpful, and ubiquitous computing devices like smartphones can be leveraged as proxies to unobtrusively analyse the human behaviour. To this aim, we propose the implementation of a real-time sleep recognition methodology relied on a smartphone equipped with a mobile app that exploits contextual and usage information to infer sleep habits. As an improvement of already presented solutions, in this proposed application an initial training stage is required, during which the selected features are processed by k-Nearest Neighbors, Decision Tree, Random Forest, and Support Vector Machine classifiers, in order to select the best model for each user. Moreover, a 1st-order Markov Chain is applied to improve the recognition performance. Experimental results demonstrate the effectiveness of the proposed approach, achieving acceptable results in term of Precision, Recall, and F1-score.