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What Actually Works for Activity Recognition in Scenarios with Significant Domain Shift: Lessons Learned from the 2019 and 2020 Sussex-Huawei Challenges., , , , , , , , and . Sensors, 22 (10): 3613 (2022)Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors., , , , , , , , , and 1 other author(s). Inf. Fusion, (2020)Applying Multiple Knowledge to Sussex-Huawei Locomotion Challenge., , , , , , , , , and . UbiComp/ISWC Adjunct, page 1488-1496. ACM, (2018)A New Frontier for Activity Recognition: The Sussex-Huawei Locomotion Challenge., , , , , , , , , and . UbiComp/ISWC Adjunct, page 1511-1520. ACM, (2018)Cross-location transfer learning for the sussex-huawei locomotion recognition challenge., , , , , and . UbiComp/ISWC Adjunct, page 730-735. ACM, (2019)Real-time Eating Detection Using a Smartwatch., , , and . EWSN, page 247-252. ACM, (2020)Mobile nutrition monitoring for well-being., , , , and . UbiComp/ISWC Adjunct, page 1194-1197. ACM, (2019)Winning the Sussex-Huawei Locomotion-Transportation Recognition Challenge., , , , , , , , , and 1 other author(s). Human Activity Sensing, Springer, (2019)Exploring Dietary Intake Data collected by FPQ using Unsupervised Learning., , , , , and . IEEE BigData, page 5126-5130. IEEE, (2019)Comparison of Feature Selection Algorithms for Minimization of Target Specific FFQs., , and . IEEE BigData, page 3592-3595. IEEE, (2020)