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A Data Mining Approach to Predict Falls in Humanoid Robot Locomotion.

, , , and . ROBOT (2), volume 418 of Advances in Intelligent Systems and Computing, page 273-285. Springer, (2015)

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Development of a Strategy to Predict and Detect Falls Using Wearable Sensors., , , and . J. Medical Syst., 43 (5): 134:1-134:15 (2019)Genetic Programming Applied to Biped Locomotion Control with Sensory Information., , , , and . ICINCO (1), page 53-62. SciTePress, (2014)Complete Inertial Pose Dataset: from raw measurements to pose with low-cost and high-end MARG sensors., , , , and . CoRR, (2022)Adapting Biped Locomotion to Sloped Environments - Combining Reinforcement Learning with Dynamical Systems., , , and . J. Intell. Robotic Syst., 80 (3-4): 625-640 (2015)Reinforcement learning approach to locomotion adaptation in sloped environments., , and . ICARSC, page 164-169. IEEE, (2014)Toward a flexible framework for learning: F3L., , and . Robotics Auton. Syst., (2017)Markerless Gait Analysis Vision System for Real-time Gait Monitoring., , , , , , , , , and . ICARSC, page 269-274. IEEE, (2020)A Data Mining Approach to Predict Falls in Humanoid Robot Locomotion., , , and . ROBOT (2), volume 418 of Advances in Intelligent Systems and Computing, page 273-285. Springer, (2015)Skill Memory in Biped Locomotion - Using Perceptual Information to Predict Task Outcome., , and . J. Intell. Robotic Syst., 82 (3-4): 379-397 (2016)A DMPs-based Approach for Human-Robot Collaboration Task Quality Management., , , and . ICARSC, page 226-231. IEEE, (2023)