WiSleep: Scalable Sleep Monitoring and Analytics Using Passive WiFi
Sensing
- URL: http://arxiv.org/abs/2102.03690v1
- Date: Sun, 7 Feb 2021 00:05:14 GMT
- Title: WiSleep: Scalable Sleep Monitoring and Analytics Using Passive WiFi
Sensing
- Authors: Priyanka Mary Mammen, Camellia Zakaria, Tergel Molom-Ochir, Amee
Trivedi, Prashant Shenoy, Rajesh Balan
- Abstract summary: WiSleep is a sleep monitoring and analytics platform using smartphone network connections that are passively sensed from WiFi infrastructure.
We propose an unsupervised ensemble model of Bayesian change point detection to predict sleep and wake-up times.
We show that WiSleep can process data from 20,000 users on a single commodity server, allowing it to scale to large campus populations with low server requirements.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Sleep deprivation is a public health concern that significantly impacts one's
well-being and performance. Sleep is an intimate experience, and
state-of-the-art sleep monitoring solutions are highly-personalized to
individual users. With a motivation to expand sleep monitoring at a large-scale
and contribute sleep data to public health understanding, we present WiSleep, a
sleep monitoring and analytics platform using smartphone network connections
that are passively sensed from WiFi infrastructure. We propose an unsupervised
ensemble model of Bayesian change point detection to predict sleep and wake-up
times. Then, we validate our approach using ground truth from a user study in
campus dormitories and a private home. Our results find WiSleep outperforming
established methods for users with irregular sleep patterns while yielding
comparable accuracy for regular sleepers with an average 79.5\% accuracy. This
is comparable to client-side based methods, albeit utilizing only
coarse-grained information. Finally, we show that WiSleep can process data from
20,000 users on a single commodity server, allowing it to scale to large campus
populations with low server requirements.
Related papers
- SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic
Social Networks [1.622340939868235]
We propose SleepNet, a system that exploits social contagion in sleep behavior through graph networks.
Our architecture overcomes the limitations of large-scale graphs containing connections irrelevant to sleep behavior by devising an attention mechanism.
arXiv Detail & Related papers (2024-01-20T04:38:34Z) - Sleep Activity Recognition and Characterization from Multi-Source
Passively Sensed Data [67.60224656603823]
Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes.
We propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes.
Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner.
arXiv Detail & Related papers (2023-01-17T15:18:45Z) - Heterogeneous Hidden Markov Models for Sleep Activity Recognition from
Multi-Source Passively Sensed Data [67.60224656603823]
Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time.
Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles.
Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
arXiv Detail & Related papers (2022-11-08T17:29:40Z) - SleepMore: Sleep Prediction at Scale via Multi-Device WiFi Sensing [0.0]
We propose SleepMore, an accurate and easy-to-deploy sleep-tracking approach based on machine learning over the user's WiFi network activity.
We validate SleepMore using data from a month-long user study involving 46 college students and draw comparisons with the Oura Ring wearable.
Our results demonstrate that SleepMore produces statistically indistinguishable sleep statistics from the Oura ring baseline for predictions made within a 5% uncertainty rate.
arXiv Detail & Related papers (2022-10-24T16:42:56Z) - Personalised recommendations of sleep behaviour with neural networks
using sleep diaries captured in Sleepio [11.243440695021567]
In collaboration with Big Health, we have analysed data from a random sample of 401,174 sleep diaries.
We have built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner.
We show that the neural network can be used to produce personalised recommendations of what sleep habits users should follow to maximise sleep quality.
arXiv Detail & Related papers (2022-07-29T18:29:05Z) - Using Ballistocardiography for Sleep Stage Classification [2.360019611990601]
Current methods of sleep stage detection are expensive, invasive to a person's sleep, and not practical in a modern home setting.
Ballistocardiography (BCG) is a non-invasive sensing technology that collects information by measuring the ballistic forces generated by the heart.
We propose to implement a sleep stage detection algorithm and compare it against sleep stages extracted from a Fitbit Sense Smart Watch.
arXiv Detail & Related papers (2022-02-02T14:02:48Z) - In-Bed Person Monitoring Using Thermal Infrared Sensors [53.561797148529664]
We use 'Griddy', a prototype with a Panasonic Grid-EYE, a low-resolution infrared thermopile array sensor, which offers more privacy.
For this purpose, two datasets were captured, one (480 images) under constant conditions, and a second one (200 images) under different variations.
We test three machine learning algorithms: Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and Neural Network (NN)
arXiv Detail & Related papers (2021-07-16T15:59:07Z) - Learning Language and Multimodal Privacy-Preserving Markers of Mood from
Mobile Data [74.60507696087966]
Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care.
One promising data source to help monitor human behavior is daily smartphone usage.
We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
arXiv Detail & Related papers (2021-06-24T17:46:03Z) - A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep [19.49661647406365]
Studies are being conducted for sleep monitoring and have now become an important tool for understanding sleep behavior.
The gold standard method for sleep analysis is polysomnography (PSG) conducted in a clinical environment but this method is both expensive and complex for long-term use.
Various solutions have been proposed using both wearable and non-wearable methods which are cheap and easy to use for in-home sleep monitoring.
arXiv Detail & Related papers (2021-04-27T04:12:43Z) - Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel
EEG Signal [63.18666008322476]
Sleep problems are one of the major diseases all over the world.
Basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep.
Specialists have to score the different signals according to one of the standard guidelines.
arXiv Detail & Related papers (2021-03-30T09:59:56Z) - Automatic detection of microsleep episodes with deep learning [55.41644538483948]
Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs)
maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance.
MSEs are mostly not considered in the absence of established scoring criteria defining MSEs.
We aimed for automatic detection of MSEs with machine learning based on raw EEG and EOG data as input.
arXiv Detail & Related papers (2020-09-07T11:38:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.