Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data

• URL: http://arxiv.org/abs/2301.10156v1
• Date: Tue, 17 Jan 2023 15:18:45 GMT
• Title: Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data
• Authors: Mar\'ia Mart\'inez-Garc\'ia, Fernando Moreno-Pino, Pablo M. Olmos, Antonio Art\'es-Rodr\'iguez
• Abstract summary: 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.
• Score: 67.60224656603823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sleep constitutes a key indicator of human health, performance, and quality of life. Sleep deprivation has long been related to the onset, development, and worsening of several mental and metabolic disorders, constituting an essential marker for preventing, evaluating, and treating different health conditions. Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. In this work, 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, in contrast to traditional sleep assessment methods that usually rely on intrusive and subjective procedures. A Heterogeneous Hidden Markov Model is used to model a discrete latent variable process associated with the Sleep Activity Recognition task in a self-supervised way. We validate our results against sleep metrics reported by tested wearables, proving the effectiveness of the proposed approach and advocating its use to assess sleep without more reliable sources.

Related papers

• Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysis [1.9662978733004597]
  This study aims to construct a machine learning-based sleep assessment model providing evidence-based assessments, such as poor sleep due to frequent movement during sleep onset. Extracting sleep sound events, deriving latent representations using VAE, clustering with GMM, and training LSTM for subjective sleep assessment achieved a high accuracy of 94.8% in distinguishing sleep satisfaction.
  arXiv  Detail & Related papers  (2024-04-16T05:56:41Z)
• 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)
• 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)
• 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)
• Identity and Posture Recognition in Smart Beds with Deep Multitask Learning [8.422257363944295]
  We propose a robust deep learning model capable of accurately detecting subjects and their sleeping postures. A combination of loss functions is used to discriminate subjects and their sleeping postures simultaneously. The proposed algorithm can ultimately be used in clinical and smart home environments.
  arXiv  Detail & Related papers  (2021-04-05T21:21:54Z)
• 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)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data [75.23250968928578]
  Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
  arXiv  Detail & Related papers  (2020-09-25T01:50:15Z)
• Sleep Stage Scoring Using Joint Frequency-Temporal and Unsupervised Features [5.104181562775778]
  A number of Automatic Sleep Stage Recognition (ASSR) methods have been proposed. Most of these methods use temporal-frequency features that have been extracted from the vital signals. Recently, some ASSR methods have been proposed which use deep neural networks for unsupervised feature extraction. In this paper, we proposed to combine the two ideas and use both temporal-frequency and unsupervised features at the same time.
  arXiv  Detail & Related papers  (2020-04-10T02:00:29Z)

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.