Published on in Vol 4, No 4 (2016): Oct-Dec

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sleep Quality Prediction From Wearable Data Using Deep Learning

Journals

  1. Muhammed T, Mehmood R, Albeshri A, Katib I. UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities. IEEE Access 2018;6:32258 View
  2. Kim I, Oh J. Deep learning: from chemoinformatics to precision medicine. Journal of Pharmaceutical Investigation 2017;47(4):317 View
  3. Obinikpo A, Kantarci B. Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities. Journal of Sensor and Actuator Networks 2017;6(4):26 View
  4. Karampela M, Isomursu M, Porat T, Maramis C, Mountford N, Giunti G, Chouvarda I, Lehocki F. The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study. Journal of Medical Internet Research 2019;21(9):e14394 View
  5. Lee Y, Chou W, Chien T, Chou P, Yeh Y, Lee H. An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study. JMIR Medical Informatics 2020;8(5):e16528 View
  6. Liu X, Sun B, Zhang Z, Wang Y, Tang H, Zhu T, Ostadabbas S. Gait can reveal sleep quality with machine learning models. PLOS ONE 2019;14(9):e0223012 View
  7. Fellger A, Sprint G, Weeks D, Crooks E, Cook D. Wearable Device-Independent Next Day Activity and Next Night Sleep Prediction for Rehabilitation Populations. IEEE Journal of Translational Engineering in Health and Medicine 2020;8:1 View
  8. Chae D, Shin J, Kim S. Collaborative Adversarial Autoencoders: An Effective Collaborative Filtering Model Under the GAN Framework. IEEE Access 2019;7:37650 View
  9. Karpov Y, Karpov L, Smetanin Y. Adaptation of General Concepts of Software Testing to Neural Networks. Programming and Computer Software 2018;44(5):324 View
  10. Rönkkö K. An Activity Tracker and Its Accompanying App as a Motivator for Increased Exercise and Better Sleeping Habits for Youths in Need of Social Care: Field Study. JMIR mHealth and uHealth 2018;6(12):e193 View
  11. Johnston W, Heiderscheit B. Mobile Technology in Running Science and Medicine: Are We Ready?. Journal of Orthopaedic & Sports Physical Therapy 2019;49(3):122 View
  12. Xu H, Li P, Yang Z, Liu X, Wang Z, Yan W, He M, Chu W, She Y, Li Y, Cao D, Yan M, Zhang Z. Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards. Journal of Medical Systems 2020;44(10) View
  13. Yan Y, Chien T, Yeh Y, Chou W, Hsing S. An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study. JMIR mHealth and uHealth 2020;8(7):e17857 View
  14. Arora A, Chakraborty P, Bhatia M. Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning. Arabian Journal for Science and Engineering 2020;45(12):10793 View
  15. Mendonca F, Mostafa S, Morgado-Dias F, Julia-Serda G, Ravelo-Garcia A. A Method for Sleep Quality Analysis Based on CNN Ensemble With Implementation in a Portable Wireless Device. IEEE Access 2020;8:158523 View
  16. Jiang S, Chin K, Tsui K. A universal deep learning approach for modeling the flow of patients under different severities. Computer Methods and Programs in Biomedicine 2018;154:191 View
  17. Dorraki M, Fouladzadeh A, Salamon S, Allison A, Coventry B, Abbott D. Can C-Reactive Protein (CRP) Time Series Forecasting be Achieved via Deep Learning?. IEEE Access 2019;7:59311 View
  18. Thomas-Brown P, Martin J, Sewell C, Abel W, Gossell-Williams M. Risperidone Provides Better Improvement of Sleep Disturbances Than Haloperidol Therapy in Schizophrenia Patients With Cannabis-Positive Urinalysis. Frontiers in Pharmacology 2018;9 View
  19. Gorini A, Mazzocco K, Triberti S, Sebri V, Savioni L, Pravettoni G. A P5 Approach to m-Health: Design Suggestions for Advanced Mobile Health Technology. Frontiers in Psychology 2018;9 View
  20. Zhang H, Deng K, Li H, Albin R, Guan Y. Deep Learning Identifies Digital Biomarkers for Self-Reported Parkinson's Disease. Patterns 2020;1(3):100042 View
  21. Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology 2021;46(1):176 View
  22. Park K, Lee S, Lee S, Cho S, Wang S, Kim S, Lee E. Sleep prediction algorithm based on machine learning technology. European Neuropsychopharmacology 2019;29:S514 View
  23. Ma S, Chou W, Chien T, Chow J, Yeh Y, Chou P, Lee H. An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study. JMIR mHealth and uHealth 2020;8(5):e16747 View
  24. Arabacı M, Özkan F, Surer E, Jančovič P, Temizel A. Multi-modal egocentric activity recognition using multi-kernel learning. Multimedia Tools and Applications 2021;80(11):16299 View
  25. Fallmann S, Chen L. Computational Sleep Behavior Analysis: A Survey. IEEE Access 2019;7:142421 View
  26. M. Al-Eidan R, Al-Khalifa H, Al-Salman A. Deep-Learning-Based Models for Pain Recognition: A Systematic Review. Applied Sciences 2020;10(17):5984 View
  27. Farajtabar M, Kıcıman E, Nathan G, White R. Modeling behaviors and lifestyle with online and social data for predicting and analyzing sleep and exercise quality. International Journal of Data Science and Analytics 2019;8(4):367 View
  28. Sathyanarayana A, Srivastava J, Fernandez-Luque L. The Science of Sweet Dreams: Predicting Sleep Efficiency from Wearable Device Data. Computer 2017;50(3):30 View
  29. Sadeghi R, Banerjee T, Hughes J, Lawhorne L. Sleep quality prediction in caregivers using physiological signals. Computers in Biology and Medicine 2019;110:276 View
  30. Chu H, Liu Y, Kuo F. A Mobile Sleep-Management Learning System for Improving Students’ Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled Trial. JMIR mHealth and uHealth 2018;6(10):e11557 View
  31. Cho T, Sunarya U, Yeo M, Hwang B, Koo Y, Park C. Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy. Electronics 2019;8(12):1461 View
  32. Berrouiguet S, Ramírez D, Barrigón M, Moreno-Muñoz P, Carmona Camacho R, Baca-García E, Artés-Rodríguez A. Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study. JMIR mHealth and uHealth 2018;6(12):e197 View
  33. Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Molecular Psychiatry 2019;24(11):1583 View
  34. Bing D, Ying J, Miao J, Lan L, Wang D, Zhao L, Yin Z, Yu L, Guan J, Wang Q. Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clinical Otolaryngology 2018;43(3):868 View
  35. Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR mHealth and uHealth 2019;7(10):e14149 View
  36. Lim X, Gan K, Abd Aziz N. Deep ConvLSTM Network with Dataset Resampling for Upper Body Activity Recognition Using Minimal Number of IMU Sensors. Applied Sciences 2021;11(8):3543 View
  37. Spina G, Casale P, Albert P, Alison J, Garcia-Aymerich J, Clarenbach C, Costello R, Hernandes N, Leuppi J, Mesquita R, Singh S, Smeenk F, Tal-Singer R, Wouters E, Spruit M, den Brinker A. Nighttime features derived from topic models for classification of patients with COPD. Computers in Biology and Medicine 2021;132:104322 View
  38. Elgart M, Redline S, Sofer T. Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research. Neurotherapeutics 2021;18(1):228 View
  39. Chou P, Chien T, Yang T, Yeh Y, Chou W, Yeh C. Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study. International Journal of Environmental Research and Public Health 2021;18(8):4256 View
  40. Huchaiah M, Kasubi J. Predicting of Sleep Behaviour in Smart Homes Based on Multi-residents Using Machine Learning Techniques. SN Computer Science 2021;2(4) View

Books/Policy Documents

  1. Dincelli E, Zhou X, Yayla A, Jafarian H. Privacy Concerns Surrounding Personal Information Sharing on Health and Fitness Mobile Apps. View
  2. Ebert D, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. View
  3. Ignatov A, Timofte R, Chou W, Wang K, Wu M, Hartley T, Van Gool L. Computer Vision – ECCV 2018 Workshops. View
  4. El-Gayar O, Ambati L, Nawar N. AI and Big Data’s Potential for Disruptive Innovation. View
  5. Ozogur G, Erturk M, Aydin M. International Telecommunications Conference. View
  6. Turner J, Floyd M, Gupta K, Oates T. Case-Based Reasoning Research and Development. View
  7. Sivak E, Smirnov I. Social Informatics. View
  8. Lingwal S, Rauthan J, Negi B. Proceedings of Integrated Intelligence Enable Networks and Computing. View
  9. Dincelli E, Zhou X, Yayla A, Jafarian H. Research Anthology on Privatizing and Securing Data. View