Personalized User Modelling for Sleep Insight (original) (raw)

Personalized User Modelling for Context-Aware Lifestyle Recommendations to Improve Sleep

2020

Sleep is a significant contributor to leading a healthy lifestyle. Each day, most people go to sleep without any idea about how their night’s rest will be or how they can leverage their data to improve it. For an activity that humans spend near a third of their life doing, there is a surprising amount of mystery around it. Despite current research, creating personalized sleep models in real-world settings has been challenging. Existing literature provides several connections between daily activities and sleep quality. Unfortunately, these insights do not generalize well in many individuals. For these reasons, it is essential to create a data-driven personalized sleep model. This research proposes a sleep model that captures causal relationships between daily activities and sleep quality and presents the user with specific feedback recommendations to improve sleep quality. Using N-of-1 experiments on longitudinal user data and event mining, the model generates a probabilistic underst...

N=1 Modelling of Lifestyle Impact on SleepPerformance

2020

Sleep is critical to leading a healthy lifestyle. Each day, most people go to sleep without any idea about how their night's rest is going to be. For an activity that humans spend around a third of their life doing, there is a surprising amount of mystery around it. Despite current research, creating personalized sleep models in real-world settings has been challenging. Existing literature provides several connections between daily activities and sleep quality. Unfortunately, these insights do not generalize well in many individuals. For these reasons, it is important to create a personalized sleep model. This research proposes a sleep model that can identify causal relationships between daily activities and sleep quality and present the user with specific feedback about how their lifestyle affects their sleep. Our method uses N-of-1 experiments on longitudinal user data and event mining to generate understanding between lifestyle choices (exercise, eating, circadian rhythm) and...

PARIS: Personalized Activity Recommendation for Improving Sleep Quality

2021

The quality of sleep has a deep impact on people’s physical and mental health. People with insufficient sleep are more likely to report physical and mental distress, activity limitation, anxiety, and pain. Moreover, in the past few years, there has been an explosion of applications and devices for activity monitoring and health tracking. Signals collected from these wearable devices can be used to study and improve sleep quality. In this paper, we utilize the relationship between physical activity and sleep quality to find ways of assisting people improve their sleep using machine learning techniques. People usually have several behavior modes that their bio-functions can be divided into. Performing time series clustering on activity data, we find cluster centers that would correlate to the most evident behavior modes for a specific subject. Activity recipes are then generated for good sleep quality for each behavior mode within each cluster. These activity recipes are supplied to a...

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.

Opportunities for computing to support healthy sleep behavior

2010

Getting the right amount of quality sleep is one of the key aspects of good health, along with a healthy diet and regular exercise. We conducted a literature review and formative study aimed at uncovering the opportunities for technology to support healthy sleep behaviors. We present the results of interviews with sleep experts, a large survey, and interviews with potential users that indicate what people would find practical and useful for sleep. We identified a number of functional and non-functional requirements for technology for sleep. We explored three possible technology ideas for healthy sleep behaviors: a sleep tracking tool, game to promote sleep, and sleep condition assessment tool.

Empowering Wearable Sensor Generated Data to Predict Changes in Individual's Sleep Quality

The 6th International Conference on Information and Communication Technology (ICoICT 2018)), 2018

Wearable sensors found in popular wrist wearable device are both generating sales profit and constantly generating vast amount of data. Some of these wearable sensors are able to record physical activity and sleep trends, both are being mainly used to give insight to its users about their current and past health and well-being. We proposed a method of data pre-processing and machine learning using simple k-nearest neighbor classifier to furthermore empower the usage of such data to predict changes in one's sleep quality based on his or her current physical activity level. Our method were challenged to predict changes in five medically-approved sleep quality indicators, using data generated by commercially available consumer-grade wrist wearable device. The experiment result shows that the successful prediction of changes in sleep quality using wearable sensor generated data can be achieved by successfully selecting and sometimes combining the right input parameter(s). Each sleep quality indicators calls for different input parameter or combined parameters. By selecting and combining the right parameter(s), our method had successfully predict changes in both sleep duration and sleep efficiency with accuracy of 68% and 64%, respectively.

#Sleep_as_Android: Feasibility of Using Sleep Logs on Twitter for Sleep Studies

2016 IEEE International Conference on Healthcare Informatics (ICHI), 2016

Social media enjoys a growing popularity as a platform to seek and share personal health information. For sleep studies using data from social media, most researchers focused on inferring sleep-related artifacts from self-reported anecdotal pointers to sleep patterns or issues such as insomnia. The data shared by "quantified-selfers" on social media presents an opportunity to study more quantitative and objective measures of sleep. We propose and validate the approach of collecting and analyzing sleep logs that are generated and shared through a sleep-tracking mobile application. We highlight the value of this data by combining it with users' social media data. The results provide a validation of using social media for sleep studies as the collected sleep data is aligned with sleep data from other sources. The results of combining social media data with sleep data provide preliminary evidence that higher social media activity is associated with lower sleep duration and quality.

Toss ‘n’ Turn: Smartphone as Sleep and Sleep Quality Detector

The rapid adoption of smartphones along with people’s propensity to keep thee in their bedrooms at night presents an opportunity to use this device as a sleep detector. This ability is valuable for UbiComp systems in terms of user context, for personal informatics, and for healthcare as sleep is correlated with many health issues. To assess this opportunity, we collected one month of phone sensor and sleep diary entries from 27 people who have a variety of sleep contexts. We used this data to construct models that detect sleep and wake states, daily sleep quality, and global sleep quality. Our system classifies sleep state with 93.06% accuracy, daily sleep quality with 83.97% accuracy, and overall sleep quality with 81.48% accuracy. Individual-user models performed better than generally trained models; to expect reasonably good prediction performance of the individual models, a user has to manually supply 3 days of training data for sleep detection and 3 weeks of training data for quality inference. Best-performing features included noise peaks and movement variations.

Predicting sleeping behaviors in long-term studies with wrist-worn sensor data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011

This paper conducts a preliminary study in which sleeping behavior is predicted using long-term activity data from a wearable sensor. For this purpose, two scenarios are scrutinized: The first predicts sleeping behavior using a day-of-the-week model. In a second scenario typical sleep patterns for either working or weekend days are modeled. In a continuous experiment over 141 days (6 months), sleeping behavior is characterized by four main features: the amount of motion detected by the sensor during sleep, the duration of sleep, and the falling asleep and waking up times. Prediction of these values can be used in behavioral sleep analysis and beyond, as a component in healthcare systems.

Real-world longitudinal data collected from the SleepHealth mobile app study

Scientific Data, 2020

Conducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track disease manifestation and long-term trajectory in this manner is quite practical and largely untapped. Researchers can assess large study cohorts using surveys and sensor-based activities that can be interspersed with participants’ daily routines. In addition, this approach offers a medium for researchers to collect contextual and environmental data via device-based sensors, data aggregator frameworks, and connected wearable devices. The main aim of the SleepHealth Mobile App Study (SHMAS) was to gain a better understanding of the relationship between sleep habits and daytime functioning utilizing a novel digital health approach. Secondary goals included assessing the feasibility of a fully-remote approach to obtaining clinical characteristics of participants, evaluating data validity, ...