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Mining hidden correlations between sleep and lifestyle factors from quantified-self data.

, , and . UbiComp Adjunct, page 547-552. ACM, (2016)

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Framing Self-quantification for Individual-level Preventive Health Care., and . HEALTHINF, page 336-343. SciTePress, (2015)Achieving Accurate Ubiquitous Sleep Sensing with Consumer Wearable Activity Wristbands Using Multi-class Imbalanced Classification., and . DASC/PiCom/DataCom/CyberSciTech, page 768-775. IEEE, (2019)Validity of Consumer Activity Wristbands and Wearable EEG for Measuring Overall Sleep Parameters and Sleep Structure in Free-Living Conditions., and . J. Heal. Informatics Res., 2 (1-2): 152-178 (2018)Nurturing wearable and mHealth technologies for self-care: Mindset, tool set and skill set., , , and . HealthCom, page 1-5. IEEE, (2016)Mining hidden correlations between sleep and lifestyle factors from quantified-self data., , and . UbiComp Adjunct, page 547-552. ACM, (2016)A Linear Performance-Breakdown Model for GPU Programming Optimization Guidance., and . IPDPS Workshops, page 596-603. IEEE Computer Society, (2014)Investigating Classroom Activities in English Conversation Lessons Based on Activity Coding and Data Visualization., , , and . JSAI-isAI Workshops, volume 10838 of Lecture Notes in Computer Science, page 375-389. Springer, (2017)Validation of Omron Wearable Blood Pressure Monitor HeartGuideTM in Free-Living Environments., and . MobiHealth, volume 362 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 339-350. Springer, (2020)Combining Resampling and Machine Learning to Improve Sleep-Wake Detection of Fitbit Wristbands., and . ICHI, page 1-3. IEEE, (2019)A Two-stage Imbalanced Learning Method for Sleep Stages Classification using Consumer Activity Trackers., and . HEALTHINF, page 633-640. SCITEPRESS, (2020)