Thi Phuoc Van Nguyen
ABSTRACT
The development of remote/wearable sensors enables more research in the health care area. Based on these kinds of sensors, the information of human's active level, health parameters can be collected to predict one's health status. Sleeping quality is an important factor to make a person feel healthy. In this work, we summarize the current models to predict sleeping quality. Inputs of those models could be environmental factors, activities, or time-series data from wearable sensors. The characteristic of the input data may lead to the choice of prediction models. The domain of data that was used to forecast sleeping quality will be considered carefully in parallel with the prediction model. Challenges and future work for this research direction will be discussed in this paper.
- Mariam Aly and Morris Moscovitch, "The effects of sleep on episodic memory in older and younger adults," Memory, vol. 18, no. 3, pp. 327--334, 2010.Google Scholar
Cross Ref
- Daniela Tempesta, Luigi De Gennaro, Vincenzo Natale, and Michele Ferrara, "Emotional memory processing is influenced by sleep quality," Sleep medicine,vol. 16, no. 7, pp. 862--870, 2015.Google ScholarCross Ref
- Adam P Knowlden, Manoj Sharma, and Vinayak K Nahar, "Using multitheory model of health behavior change to predict adequate sleep behavior," Family & community health, vol. 40, no. 1, pp. 56--61, 2017.Google Scholar
- Juha M Kortelainen, Martin O Mendez, Anna Maria Bianchi, Matteo Matteucci, and Sergio Cerutti, "Sleep staging based on signals acquired through bed sensor," IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 3, pp.776--785, 2010.Google ScholarDigital Library
- Alexander Chan and Ravi Narasimhan, "Automated sleep staging using wearable sensors," May 23 2017, US Patent 9,655,559.Google Scholar
- Minh-Son Dao, Tuan-Anh Nguyen-Gia, and Van-Cuong Mai, Daily human activities recognition using heterogeneous sensors from smartphones," Procedia computer science, vol. 111, pp. 323--328, 2017.Google Scholar
- Tomohiro Sato, Minh-Son Dao, Kota Kuribayashi, and Koji Zettsu, "Sephla: challenges and opportunities within environment-personal health archives," in International Conference on Multimedia Modeling. Springer, 2019, pp. 325--337.Google Scholar
- Vanessa Ibáñez, Josep Silva, and Omar Cauli, ?A survey on sleep assessmentmethods,"PeerJ, vol. 6, pp. e4849, 2018.Google Scholar
- Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias, Antonio Gravelo-Garcia, and Thomas Penzel, "A review of approaches for sleep quality analysis," IEEE Access, vol. 7, pp. 24527--24546, 2019.Google ScholarCross Ref
- Aria R Ruggiero, Hannah D Peach, and Jane F Gaultney, "Association of sleep attitudes with sleep hygiene, duration, and quality: a survey exploration of the moderating effect of age, gender, race, and perceived socioeconomic status," Health Psychology and Behavioral Medicine, vol. 7, no. 1, pp. 19--44, 2019.Google ScholarCross Ref
- Mohamed Abdel-Basset, Weiping Ding, and Laila Abdel-Fatah, "The fusion of internet of intelligent things (ioit) in remote diagnosis of obstructive sleep apnea: A survey & a new model," Information Fusion, 2020.Google Scholar
- Sonali Bose, Kristie R Ross, Maria J Rosa, Yueh-Hsiu Mathilda Chiu, Allan Just, Itai Kloog, Ander Wilson, Jennifer Thompson, Katherine Svensson, Martha María Téllez Rojo, et al., "Prenatal particulate air pollution exposure and sleep disruption in preschoolers: Windows of susceptibility," Environment international, vol. 124, pp. 329--335, 2019.Google ScholarCross Ref
- Trinidad Sánchez, David Gozal, Dale L Smith, Catalina Foncea, Carmen Betancur, and Pablo E Brockmann, "Association between air pollution and sleep disordered breathing in children," Pediatric Pulmonology, vol. 54, no. 5, pp. 544--550, 2019.Google ScholarCross Ref
- Yinyu Xie, Hao Xiang, Niu Di, Zhenxing Mao, Jian Hou, Xiaotian Liu, WenqianHuo, Boyi Yang, Guanghui Dong, Chongjian Wang, et al., "Association between residential greenness and sleep quality in chinese rural population," Environment International, vol. 145, pp. 106100, 2020.Google ScholarCross Ref
- Hongjun Yu, Panpan Chen, Shelby Paige Gordon, Miao Yu, and Yangyang Wang, "The association between air pollution and sleep duration: A cohort study of freshmen at a university in beijing, china," International Journal of Environmental Research and Public Health, vol. 16, no. 18, pp. 3362, 2019.Google ScholarCross Ref
- Aarti Sathyanarayana, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, and Shahrad Taheri, "Sleep quality prediction from wearable data using deep learning," JMIR mHealth and uHealth, vol. 4, no. 4, pp. e125, 2016.Google Scholar
- Dhruv Upadhyay, Vaibhav Pandey, Nitish Nag, and Ramesh Jain, "N= 1 modelling of lifestyle impact on sleep performance," arXiv preprint arXiv:2006.10884, 2020.Google Scholar
- Dayna A Johnson, Guido Simonelli, Kari Moore, Martha Billings, Mahasin S Mujahid, Michael Rueschman, Ichiro Kawachi, Susan Redline, Ana V Diez Roux, and Sanjay R Patel, "The neighborhood social environment and objective measures of sleep in the multi-ethnic study of atherosclerosis," Sleep, vol. 40, no. 1, pp. zsw016, 2017.Google ScholarCross Ref
- Gongbo Chen, Shanshan Li, Luke D Knibbs, Nicholas AS Hamm, Wei Cao, Tiantian Li, Jianping Guo, Hongyan Ren, Michael J Abramson, and Yuming Guo, "A machine learning method to estimate pm2. 5 concentrations across china with remote sensing, meteorological and land use information,"Science of the Total Environment, vol. 636, pp. 52--60, 2018.Google ScholarCross Ref
- Olivia Walch, Yitong Huang, Daniel Forger, and Cathy Goldstein, "Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device," Sleep, vol. 42, no. 12, pp. zsz180, 2019.Google ScholarCross Ref
- Pekka Siirtola, Heli Koskimäki, Henna Mönttinen, and Juha Röning, "Using sleep time data from wearable sensors for early detection of migraine attacks," Sensors, vol. 18, no. 5, pp. 1374, 2018.Google ScholarCross Ref
- Reza Sadeghi, Tanvi Banerjee, Jennifer C Hughes, and Larry W Lawhorne, "Sleep quality prediction in caregivers using physiological signals," Computers in biology and medicine, vol. 110, pp. 276--288, 2019.Google Scholar
- Mark S Aloia, Matthew S Goodwin, Wayne F Velicer, J Todd Arnedt, Molly Zimmerman, Jaime Skrekas, Sarah Harris, and Richard P Millman, "Time series analysis of treatment adherence patterns in individuals with obstructive sleep apnea," Annals of Behavioral Medicine, vol. 36, no. 1, pp. 44--53, 2008.Google ScholarCross Ref
- Joo-Chang Kim and Kyungyong Chung, "Mining based time-series sleeping pattern analysis for life big-data," Wireless Personal Communications, vol. 105, no.2, pp. 475--489, 2019.Google ScholarDigital Library
- Anshika Arora, Pinaki Chakraborty, and MPS Bhatia, "Analysis of data from wearable sensors for sleep quality estimation and prediction using deep learning," Arabian Journal for Science and Engineering, pp. 1--20, 2020.Google Scholar
- Karin Sygna, Gunn Marit Aasvang, Geir Aamodt, Bente Oftedal, and Norun Hjertager Krog, "Road traffic noise, sleep and mental health," Environmental research, vol. 131, pp. 17--24, 2014.Google ScholarCross Ref
- Sattamat Lappharat, Nutta Taneepanichskul, Sirimon Reutrakul, and Naricha Chirakalwasan, "Effects of bedroom environmental conditions on the severity of obstructive sleep apnea," Journal of Clinical Sleep Medicine, vol. 14, no. 4, pp. 565--573, 2018.Google ScholarCross Ref
- Zitao Liu and Milos Hauskrecht, "A personalized predictive framework for multivariate clinical time series via adaptive model selection," in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017, pp. 1169--1177.Google Scholar
- Martin O Mendez, Matteo Migliorini, Juha M Kortelainen, Domenino Nistico,Edgar Arce-Santana, Sergio Cerutti, and Anna M Bianchi, "Evaluation of the sleep quality based on bed sensor signals: Time-variant analysis," in2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE,2010, pp. 3994--3997.Google Scholar
- William Romine, Tanvi Banerjee, and Garrett Goodman, "Toward sensor-based sleep monitoring with electrodermal activity measures," Sensors, vol. 19, no. 6,pp. 1417, 2019.Google ScholarCross Ref
- Zachary Beattie, Y Oyang, A Statan, A Ghoreyshi, A Pantelopoulos, A Russell, and CJPM Heneghan, "Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals," Physiological measurement, vol. 38, no. 11, pp. 1968, 2017.Google ScholarCross Ref
- Xiaoqian Jiang, Aziz A Boxwala, Robert El-Kareh, Jihoon Kim, and Lucila Ohno-Machado, "A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support," Journal of the American Medical Informatics Association, vol. 19, no. e1, pp. e137--e144, 2012.Google ScholarCross Ref
- Dagmara Dimitriou, Frances Le Cornu Knight, and Patrick Milton, "The role of environmental factors on sleep patterns and school performance in adolescents," Frontiers in psychology, vol. 6, pp. 1717, 2015.Google ScholarCross Ref
Index Terms
- Models to Predict Sleeping Quality from Activities and Environment: Current Status, Challenges and Opportunities
Recommendations
Feature-based Method for Nurse Care Complex Activity Recognition from Accelerometer Sensor
UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable ComputersAs the number of patients in hospitals is increasing day by day, proper monitoring and hospital care towards patients must be ensured. In this regard, Nurse care activity recognition can play a significant role in improving the existing healthcare ...
A Systematic Review of Wearable Patient Monitoring Systems --- Current Challenges and Opportunities for Clinical Adoption
The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing ...
Predicting dementia with routine care EMR data
Highlights- Dementia can be predicted from EMR routine care data with a level of accuracy suitable for automated pre-screening.
AbstractOur aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional ...
Comments