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, 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
  41. Kaufmann C, Lee E, Wing D, Sutherland A, Christensen C, Ancoli-Israel S, Depp C, Yoon H, Soontornniyomkij B, Eyler L. Correlates of poor sleep based upon wrist actigraphy data in bipolar disorder. Journal of Psychiatric Research 2021;141:385 View
  42. Guo B, Ma Y, Yang J, Wang Z, Abdulhay E. Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning. Journal of Healthcare Engineering 2021;2021:1 View
  43. Bitkina O, Park J, Kim J. Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods. International Journal of Environmental Research and Public Health 2022;19(16):9890 View
  44. Cohen A, Nahed B. The Digital Neurologic Examination. Digital Biomarkers 2021;5(1):114 View
  45. Lin C, Chien T, Chen Y, Lee Y, Su S. An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel. Medicine 2022;101(4):e28697 View
  46. Fritz H, Tang M, Kinney K, Nagy Z. Evaluating machine learning models to classify occupants’ perceptions of their indoor environment and sleep quality from indoor air quality. Journal of the Air & Waste Management Association 2022;72(12):1381 View
  47. Nassibi A, Papavassiliou C, Atashzar S. Depression diagnosis using machine intelligence based on spatiospectrotemporal analysis of multi-channel EEG. Medical & Biological Engineering & Computing 2022;60(11):3187 View
  48. Mitrea D, Tamas L. Manufacturing Execution System Specific Data Analysis-Use Case With a Cobot. IEEE Access 2018;6:50245 View
  49. Mendonca F, Mostafa S, Morgado-Dias F, Ravelo-Garcia A, Penzel T. A Review of Approaches for Sleep Quality Analysis. IEEE Access 2019;7:24527 View
  50. Dini Kounoudes A, Kapitsaki G, Katakis I. Enhancing user awareness on inferences obtained from fitness trackers data. User Modeling and User-Adapted Interaction 2023;33(4):967 View
  51. Khowaja K, Syed W, Singh M, Taheri S, Chagoury O, Al-Thani D, Aupetit M. A Participatory Design Approach to Develop Visualization of Wearable Actigraphy Data for Health Care Professionals: Case Study in Qatar. JMIR Human Factors 2022;9(2):e25880 View
  52. Chakrabarti S, Biswas N, Jones L, Kesari S, Ashili S. Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics 2022;12(9):2110 View
  53. Hsu C, Chien T, Yan Y. An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing. Medicine 2021;100(52):e28457 View
  54. Cohen Zion M, Gescheit I, Levy N, Yom-Tov E. Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire. Journal of Medical Internet Research 2022;24(11):e41288 View
  55. Park K, Lee S, Lee C, Hwang H, Yoon D, Choi E, Lee E. Prediction of good sleep with physical activity and light exposure: a preliminary study. Journal of Clinical Sleep Medicine 2022;18(5):1375 View
  56. Arora A, Chakraborty P, Bhatia M. Intervention of Wearables and Smartphones in Real Time Monitoring of Sleep and Behavioral Health: An Assessment Using Adaptive Neuro-Fuzzy Technique. Arabian Journal for Science and Engineering 2022;47(2):1999 View
  57. Óskarsdóttir M, Islind A, August E, Arnardóttir E, Patou F, Maier A. Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study. JMIR Formative Research 2022;6(2):e31807 View
  58. Edney S, Park S, Tan L, Chua X, Dickens B, Rebello S, Petrunoff N, Müller A, Tan C, Müller-Riemenschneider F, van Dam R. Advancing understanding of dietary and movement behaviours in an Asian population through real-time monitoring: Protocol of the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA). DIGITAL HEALTH 2022;8:205520762211105 View
  59. Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes L, Dou D. From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques. IEEE Internet of Things Journal 2022;9(17):15413 View
  60. Fukui K, Ishimaru S, Kato T, Numao M. Sound-based sleep assessment with controllable subject-dependent embedding using Variational Domain Adversarial Neural Network. International Journal of Data Science and Analytics 2023 View
  61. Arji G, Erfannia L, alirezaei S, Hemmat M. A systematic literature review and analysis of deep learning algorithms in mental disorders. Informatics in Medicine Unlocked 2023;40:101284 View
  62. Migovich M, Ullal A, Fu C, Peters S, Sarkar N. Feasibility of wearable devices and machine learning for sleep classification in children with Rett syndrome: A pilot study. DIGITAL HEALTH 2023;9 View
  63. Wijaya J, Andersen P, Surantha N. Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device. Procedia Computer Science 2023;227:1153 View
  64. Demrozi F, Pravadelli G, Bihorac A, Rashidi P. Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey. IEEE Access 2020;8:210816 View
  65. Shahid Z, Saguna S, Åhlund C. Multiarmed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes. IEEE Internet of Things Journal 2024;11(3):4414 View
  66. Arora A. IoT-Enabled Smart Mental Health Assessment Using Deep Hybrid Regression Models Over Actigraph-Based Sequential Motor Activity Data. Arabian Journal for Science and Engineering 2024 View
  67. Lamichhane B, Zhou J, Sano A. Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model. IEEE Journal of Biomedical and Health Informatics 2023;27(7):3246 View
  68. Khalid M, Klerman E, McHill A, Phillips A, Sano A. SleepNet. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(1):1 View
  69. Yang H, Ge M, Xiang K, Bai X, Li H. FedVAE: Communication-Efficient Federated Learning With Non-IID Private Data. IEEE Systems Journal 2023;17(3):4798 View
  70. Su C, Ko L, Jung T, Onton J, Tzou S, Juang J, Hsu C. Extracting Stress-Related EEG Patterns From Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024;32:1817 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
  10. Köse U, Ersoy M, Türkçetin A. Trends in Data Engineering Methods for Intelligent Systems. View
  11. Zulfiker M, Kabir N, Biswas A, Chakraborty P. Advances in Computing and Data Sciences. View
  12. Marastoni N, Oliboni B, Quintarelli E. Big Data Analytics and Knowledge Discovery. View
  13. Ling T, Zhu X, Zhou X, Wang S. Wireless Algorithms, Systems, and Applications. View
  14. Azad D, Shreyansh K, Adarsh M, Kumari A, Nirmala M, Poornima A. Innovative Data Communication Technologies and Application. View
  15. Arora A, Chakraborty P, Bhatia M. Proceedings of International Conference on Communication and Computational Technologies. View
  16. Van N, Son D, Zettsu K. Sensing Technology. View
  17. Oliboni B, Dalla Vecchia A, Marastoni N, Quintarelli E. Advances in Smart Healthcare Paradigms and Applications. View
  18. Kabir R, Syed H, Vinnakota D, Sivasubramanian M, Hitch G, Okello S, Sharon-Shivuli-Isigi , Pulikkottil A, Mahmud I, Dehghani L, Parsa A. Deep Learning in Personalized Healthcare and Decision Support. View
  19. Singh M, Gupta M, Sharma A, Jain P, Aggarwal P. Deep Learning for Healthcare Services. View