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

Journals
- 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
- Kim I, Oh J. Deep learning: from chemoinformatics to precision medicine. Journal of Pharmaceutical Investigation 2017;47(4):317 View
- 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
- 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
- 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
- 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
- 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
- Chae D, Shin J, Kim S. Collaborative Adversarial Autoencoders: An Effective Collaborative Filtering Model Under the GAN Framework. IEEE Access 2019;7:37650 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology 2021;46(1):176 View
- 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
- 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
- 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
- Fallmann S, Chen L. Computational Sleep Behavior Analysis: A Survey. IEEE Access 2019;7:142421 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Molecular Psychiatry 2019;24(11):1583 View
- 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
- 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
- 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
- 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
- Elgart M, Redline S, Sofer T. Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research. Neurotherapeutics 2021;18(1):228 View
- 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
- 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
- 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
- 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
- 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
- Cohen A, Nahed B. The Digital Neurologic Examination. Digital Biomarkers 2021;5(1):114 View
- 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
- 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
- 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
- Mitrea D, Tamas L. Manufacturing Execution System Specific Data Analysis-Use Case With a Cobot. IEEE Access 2018;6:50245 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Wijaya J, Andersen P, Surantha N. Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device. Procedia Computer Science 2023;227:1153 View
- 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
- 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
- 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;49(9):12493 View
- 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
- 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
- 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
- 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
- Trujillo R, Zhang E, Templeton J, Poellabauer C. Predicting long-term sleep deprivation using wearable sensors and health surveys. Computers in Biology and Medicine 2024;179:108749 View
- Zhang Y, Zheng X, Zhang X, Pan J, Thean A. Hybrid Integration of Wearable Devices for Physiological Monitoring. Chemical Reviews 2024;124(18):10386 View
- Park K, Lee S, Lee C, Hwang H, Yoon D, Choi E, Lee E. Predicting sleep based on physical activity, light exposure, and Heart rate variability data using wearable devices. Annals of Medicine 2024;56(1) View
- Zou R, Chen H, Pan H, Zhang H, Kong L, Zhang Z, Xiang Z, Zhi J, Xu Y. Self-powered and self-sensing wearable devices from a comfort perspective. Device 2024;2(11):100466 View
- Skaramagkas V, Kyprakis I, Karanasiou G, Fotiadis D, Tsiknakis M. A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data. IEEE Open Journal of Engineering in Medicine and Biology 2025;6:261 View
- Mostafa Monowar M, Nobel S, Afroj M, Hamid M, Uddin M, Kabir M, Mridha M. Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques. Frontiers in Artificial Intelligence 2025;7 View
- Lee J, Yang E, Cho A, Choi Y, Lee S, Lee K. Sleep efficiency in community-dwelling persons living with dementia: exploratory analysis using machine learning. Journal of Clinical Sleep Medicine 2025;21(2):393 View
- Yao L, Chen Q, Yang K, Zheng Z, Chen Z, Wang D, Xia Y, Chen D, Chen L. Novel insight into prediction model for sleep quality among college students: a LASSO-derived sleep evaluation. Frontiers in Psychiatry 2025;16 View
- Sakal C, Chen T, Xu W, Zhang W, Yang Y, Li X. Towards proactively improving sleep: machine learning and wearable device data forecast sleep efficiency 4–8 hours before sleep onset. SLEEP 2025 View
Books/Policy Documents
- Dincelli E, Zhou X, Yayla A, Jafarian H. Privacy Concerns Surrounding Personal Information Sharing on Health and Fitness Mobile Apps. View
- Ebert D, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. View
- Ignatov A, Timofte R, Chou W, Wang K, Wu M, Hartley T, Van Gool L. Computer Vision – ECCV 2018 Workshops. View
- El-Gayar O, Ambati L, Nawar N. AI and Big Data’s Potential for Disruptive Innovation. View
- Ozogur G, Erturk M, Aydin M. International Telecommunications Conference. View
- Turner J, Floyd M, Gupta K, Oates T. Case-Based Reasoning Research and Development. View
- Sivak E, Smirnov I. Social Informatics. View
- Lingwal S, Rauthan J, Negi B. Proceedings of Integrated Intelligence Enable Networks and Computing. View
- Dincelli E, Zhou X, Yayla A, Jafarian H. Research Anthology on Privatizing and Securing Data. View
- Köse U, Ersoy M, Türkçetin A. Trends in Data Engineering Methods for Intelligent Systems. View
- Zulfiker M, Kabir N, Biswas A, Chakraborty P. Advances in Computing and Data Sciences. View
- Marastoni N, Oliboni B, Quintarelli E. Big Data Analytics and Knowledge Discovery. View
- Ling T, Zhu X, Zhou X, Wang S. Wireless Algorithms, Systems, and Applications. View
- Azad D, Shreyansh K, Adarsh M, Kumari A, Nirmala M, Poornima A. Innovative Data Communication Technologies and Application. View
- Arora A, Chakraborty P, Bhatia M. Proceedings of International Conference on Communication and Computational Technologies. View
- Van N, Son D, Zettsu K. Sensing Technology. View
- Oliboni B, Dalla Vecchia A, Marastoni N, Quintarelli E. Advances in Smart Healthcare Paradigms and Applications. View
- 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
- Singh M, Gupta M, Sharma A, Jain P, Aggarwal P. Deep Learning for Healthcare Services. View
Conference Proceedings
- Fellger A, Sprint G, Andrews A, Weeks D, Crooks E. 2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT). Nighttime Sleep Duration Prediction for Inpatient Rehabilitation Using Similar Actigraphy Sequences View
- Lam P, Dinh Son N, Chi H, Phuoc Van N, Duc Minh N. 2019 13th International Conference on Sensing Technology (ICST). Novel Algorithm to Classify Sleep Stages View
- Chen C, Vhaduri S, Poellabauer C. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). Estimating Sleep Duration from Temporal Factors, Daily Activities, and Smartphone Use View
- Pintelas E, Kotsilieris T, Livieris I, Pintelas P. Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion. A review of machine learning prediction methods for anxiety disorders View
- Dorraki M, Fouladzadeh A, Allison A, Coventry B, Abbott D. 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS). Deep Learning for C-Reactive Protein Prediction View
- Pathinarupothi R, Vinaykumar R, Rangan E, Gopalakrishnan E, Soman K. 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning View
- Seth A, Babu B, Iyenger S. 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS). Machine Learning Model for Predicting Insomnia Levels in Indian College Students View
- Jiang J, Chao Z, Bertozzi A, Wang W, Young S, Needell D. Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices View
- Islam M, Masum A, Abujar S, Hossain S. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). Prediction of chronic Insomnia using Machine Learning Techniques View
- Mahesworo B, Budiarto A, Hidayat A, Soeparno H, Pardamean B. 2020 International Conference on Information Management and Technology (ICIMTech). Sleep Quality and Daily Activity Association Assessment From Wearable Device Data View
- Phan D, Chan C, Nguyen D. Proceedings of the 4th International Conference on Medical and Health Informatics. Applying Deep Learning for Prediction Sleep Quality from Wearable Data View
- Upadhyay D, Pandey V, Nag N, Jain R. Proceedings of the 1st International Workshop on Human-centric Multimedia Analysis. Personalized User Modelling for Sleep Insight View
- Panindre P, Gandhi V, Kumar S. 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). Artificial Intelligence-based Remote Diagnosis of Sleep Apnea using Instantaneous Heart Rates View
- Zhang L, Ren J, Yuan H, Yang Z, Wang W, Guo M, Cheng G, Zhou L, Tao S, Zhang L, Cui H, Fang Y. 2020 5th International Conference on Universal Village (UV). Evaluation of Smart Healthcare Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness and Sustainability View
- Kazlouski A, Marchioro T, Manifavas H, Markatos E. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). Do partner apps offer the same level of privacy protection? The case of wearable applications View
- Roslidar , Muchamad M, Arnia F, Syukri M, Munadi K. 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). A Conceptual Framework of Deploying a Trained CNN Model for Mobile Breast Self-Screening View
- Mitrea D, Tamas L. 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). MES Specific Data Analysis. Case Study with the Baxter Robot View
- Hidayat W, Tambunan T, Budiawan R. 2018 6th International Conference on Information and Communication Technology (ICoICT). Empowering Wearable Sensor Generated Data to Predict Changes in Individual's Sleep Quality View
- Tjonck K, Kancharla C, Vankeirsbilck J, Hallez H, Boydens J, Pang B. 2021 XXX International Scientific Conference Electronics (ET). Real-Time Activity Tracking using TinyML to Support Elderly Care View
- Nguyen T, Nguyen D, Zettsu K. Proceedings of the 2021 ACM Workshop on Intelligent Cross-Data Analysis and Retrieval. Models to Predict Sleeping Quality from Activities and Environment: Current Status, Challenges and Opportunities View
- Khoa T, Nguyen D, Nguyen Thi P, Zettsu K. Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval. FedMCRNN: Federated Learning using Multiple Convolutional Recurrent Neural Networks for Sleep Quality Prediction View
- Van N, Son D, Zettsu K. 2021 8th NAFOSTED Conference on Information and Computer Science (NICS). A Personalized Adaptive Algorithm for Sleep Quality Prediction using Physiological and Environmental Sensing Data View
- Chifu V, Bianca Pop C, Ciurianu A, Chifu E, Antal M. 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP). Machine Learning-based Approach for Predicting Health Information Using Smartwatch Data View
- Kazemi K, Azimi I, Liljeberg P, Rahmani A. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Can Sleep Quality Attributes be Predicted from Physical Activity in Everyday Settings? View
- Dharangutte P, Xie Z, Gao J, Schoenfeld E, Hua Y, Ye F. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). HeartInsightify: Interpreting Longitudinal Heart Rate Data for Health Insights through Conformal Clustering View
- Woo S, Kang M, Park B, Hong K. 2022 13th Asian Control Conference (ASCC). Sleep stage classification using electroencephalography via Mel frequency cepstral coefficients View
- Matias I, Wac K. 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). Dejà vu: Recurrent Neural Networks for health wearables data forecast View
- Gan W, Dao M, Zettsu K. 2022 IEEE International Conference on Big Data (Big Data). Monitoring and Improving Personalized Sleep Quality from Long-Term Lifelogs View
- Zhaoqi Z, Kumar B. 2023 International Conference on Artificial Intelligence and Smart Communication (AISC). Face Detection and Classification of Sleep State of Humans by Deep Learning Mechanisms View
- Hayat U, Iqbal Z, Mumtaz A, Abbas S. 2023 International Conference on Communication, Computing and Digital Systems (C-CODE). Machine Learning Technique to Monitor Heartbeat using Amalgamated Data of Multi-Sensor Stream View
- Kalimuthu S, Perumal T, Yaakob R, Marlisah E, Raghavan S. THE 12TH ANNUAL INTERNATIONAL CONFERENCE (AIC) 2022: The 12th Annual International Conference on Sciences and Engineering (AIC-SE) 2022. Multiple human activity recognition using iot sensors and machine learning in device-free environment: Feature extraction, classification, and challenges: A comprehensive review View
- Shahid Z, Saguna S, Ahlund C. 2024 IEEE 21st Consumer Communications & Networking Conference (CCNC). Recognizing Seasonal Sleep Patterns of Elderly in Smart Homes Using Clustering View
- Rechichi I, Gangi L, Zibetti M, Olmo G. 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). Home Monitoring of Sleep Disturbances in Parkinson’s Disease: A Wearable Solution View
- Kumar M, Ahmed B, Mishra H, Jha A, Sikarwal P, Rampal S. 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI). Predictive Sleep Disorder Modelling: Using Machine Learning with Lifestyle and Sleep Health Data View
- Ziegenbein C, Ortiz B, Gupta V, Choi S. 2024 IEEE International Conference on Big Data (BigData). Preparing Wearable Data for AI-Powered Mood and Compliance Prediction in HCT Patients and Caregivers View
- Kim S, Kim J, Shin H, Kim S. 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). Predicting Sleep Quality Using Lifelog Data with Deep Learning Techniques View
- Nasir O, Alazaidah R, Al-Safarini M, Alamleh A, Zaid A, Mansur H, Elrashidi A, Alzoubi H, Bani-Bakr A. 2024 25th International Arab Conference on Information Technology (ACIT). Classification of Sleep-Related Disorders Using Machine Learning Techniques View
- Stephen C, Islam T. 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). Lifestyle to Sleep Health: A CNN-LSTM Approach for Predicting Sleep Quality and Disorders View
- Wang Y. 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC). Application and Analysis of Deep Learning on Sleep Quality Prediction Tasks View
- Nayyem M, Raju M, Al Rakin A, Sharif K, Arafin R, Sultana S. 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). Augmenting Sleep Quality Prognostics through Internet of Things and Machine Learning: A Rigorous Comparative Analysis for Advanced Personalized Health Metrics View