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M^2G: A Monitor of Monitoring Systems with Ground Truth Validation Features for Research-Oriented Residential Applications.

, , , , , , and . MASS, page 10-18. IEEE Computer Society, (2017)

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M^2G: A Monitor of Monitoring Systems with Ground Truth Validation Features for Research-Oriented Residential Applications., , , , , , and . MASS, page 10-18. IEEE Computer Society, (2017)BESI: Reliable and Heterogeneous Sensing and Intervention for In-home Health Applications., , , , , , , , , and 1 other author(s). CHASE, page 147-156. IEEE Computer Society / ACM, (2017)Motion Biomarkers for Early Detection of Dementia-Related Agitation., , , , , , and . DigitalBioMarker@MobiSys, page 15-20. ACM, (2017)Inferring physical agitation in dementia using smartwatch and sequential behavior models., , , and . BHI, page 170-173. IEEE, (2018)Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 28531-28548. PMLR, (2023)Wearable Respiration Monitoring: Interpretable Inference With Context and Sensor Biomarkers., , and . IEEE J. Biomed. Health Informatics, 25 (6): 1938-1948 (2021)QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG., , and . EMBC, page 1-4. IEEE, (2023)Non-Invasive Inference of Minute Ventilation Using Wearable ECG and Gaussian Process Regression., , , and . BHI, page 1-4. IEEE, (2019)Besi: behavior learning and tracking with wearable and in-home sensors - a dementia case-study: poster abstract., , , , and . IoTDI, page 281-282. ACM, (2019)Multiple-Instance Learning for Sparse Behavior Modeling from Wearables: Toward Dementia-Related Agitation Prediction., , , and . EMBC, page 1330-1333. IEEE, (2019)