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A Personalized Approach for Detecting Unusual Sleep from Time Series Sleep-Tracking Data.

, , and . ICHI, page 18-23. IEEE Computer Society, (2016)

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Combining Resampling and Machine Learning to Improve Sleep-Wake Detection of Fitbit Wristbands., and . ICHI, page 1-3. IEEE, (2019)Nurturing wearable and mHealth technologies for self-care: Mindset, tool set and skill set., , , and . HealthCom, page 1-5. IEEE, (2016)A Pervasive Sensing Approach to Automatic Assessment of Trunk Coordination Using Mobile Devices., , , , , and . EAI Endorsed Trans. Pervasive Health Technol., 4 (15): e5 (2018)Mining hidden correlations between sleep and lifestyle factors from quantified-self data., , and . UbiComp Adjunct, page 547-552. ACM, (2016)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)Linear Performance-Breakdown Model: A Framework for GPU kernel programs performance analysis., and . IJNC, 5 (1): 86-104 (2015)Framing Self-quantification for Individual-level Preventive Health Care., and . HEALTHINF, page 336-343. SciTePress, (2015)Not All Errors Are Created Equal: Influence of User Characteristics on Measurement Errors of Consumer Wearable Devices for Sleep Tracking., and . EAI Endorsed Trans. Pervasive Health Technol., 4 (15): e4 (2018)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)A Personalized Approach for Detecting Unusual Sleep from Time Series Sleep-Tracking Data., , and . ICHI, page 18-23. IEEE Computer Society, (2016)