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A beverage intake tracking system based on machine learning algorithms, and ultrasonic and color sensors: poster abstract.

, , , and . IPSN, page 313-314. ACM, (2017)

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Power-Aware Activity Monitoring Using Distributed Wearable Sensors., , , , and . IEEE Trans. Hum. Mach. Syst., 44 (4): 537-544 (2014)IEEE Access Special Section Editorial: Body Area Networks., , , , , , and . IEEE Access, (2018)Toward Ultra-Low-Power Remote Health Monitoring: An Optimal and Adaptive Compressed Sensing Framework for Activity Recognition., , , , , , and . CoRR, (2023)Model-Agnostic Structural Transfer Learning for Cross-Domain Autonomous Activity Recognition., , , , and . Sensors, 23 (14): 6337 (July 2023)On-Device Machine Learning for Diagnosis of Parkinson's Disease from Hand Drawn Artifacts., , , , , and . BSN, page 1-4. IEEE, (2022)Power-aware action recognition with optimal sensor selection: an AdaBoost driven distributed template matching approach., , , and . mHealthSys@SenSys, page 5:1-5:6. ACM, (2011)Continual Learning for Activity Recognition., , and . EMBC, page 2416-2420. IEEE, (2022)CL-Gym: Full-Featured PyTorch Library for Continual Learning., and . CVPR Workshops, page 3621-3627. Computer Vision Foundation / IEEE, (2021)Dropout as an Implicit Gating Mechanism For Continual Learning., , and . CVPR Workshops, page 945-951. Computer Vision Foundation / IEEE, (2020)Understanding the Role of Training Regimes in Continual Learning., , , and . NeurIPS, (2020)