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Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition.

, , , и . Sensors, 20 (4): 1208 (2020)

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Improving human movement sensing with micro models and domain knowledge.. University College Cork, Republic of Ireland, (2021)base-search.net (ftunivcollcork:oai:cora.ucc.ie:10468/11868).Monitoring Emergency First Responders' Activities via Gradient Boosting and Inertial Sensor Data., , , , и . ECML/PKDD (3), том 11053 из Lecture Notes in Computer Science, стр. 691-694. Springer, (2018)Subject-dependent and -independent human activity recognition with person-specific and -independent models., , , и . iWOAR, стр. 5:1-5:7. ACM, (2019)Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition., , , и . Sensors, 20 (4): 1208 (2020)Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance., , , и . Sensors, 20 (13): 3647 (2020)Sensor and feature selection for an emergency first responders activity recognition system., , , и . IEEE SENSORS, стр. 1-3. IEEE, (2017)Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players., , , , , , , и . Sensors, 20 (11): 3029 (2020)Human activity recognition for emergency first responders via body-worn inertial sensors., , , и . BSN, стр. 5-8. IEEE, (2017)