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Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network.

, , and . Sensors, 19 (15): 3420 (2019)

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Contact-Free Monitoring of Physiological Parameters in People With Profound Intellectual and Multiple Disabilities., , , and . ICCV Workshops, page 1664-1672. IEEE, (2019)Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network., , and . Sensors, 19 (15): 3420 (2019)A New Frontier for Activity Recognition: The Sussex-Huawei Locomotion Challenge., , , , , , , , , and . UbiComp/ISWC Adjunct, page 1511-1520. ACM, (2018)Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach., , and . Sensors, 21 (5): 1836 (2021)Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors., , , , , , , , , and 1 other author(s). Inf. Fusion, (2020)Recognition of Behaviour Patterns for People with Profound Intellectual and Multiple Disabilities., , , and . UbiComp/ISWC Adjunct, page 523-527. ACM, (2021)Applying Multiple Knowledge to Sussex-Huawei Locomotion Challenge., , , , , , , , , and . UbiComp/ISWC Adjunct, page 1488-1496. ACM, (2018)Continuous Blood Pressure Estimation from PPG Signal., , and . Informatica (Slovenia), (2018)Winning the Sussex-Huawei Locomotion-Transportation Recognition Challenge., , , , , , , , , and 1 other author(s). Human Activity Sensing, Springer, (2019)Blood Pressure Estimation with a Wristband Optical Sensor., and . UbiComp/ISWC Adjunct, page 758-761. ACM, (2018)