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Demand-side flexibility: quantification of benefits in the EU

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Correction: Fiorini, L., et al., Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data. Sensors 2017, 17, 1034., , , , and . Sensors, 19 (22): 4860 (2019)Demand-side flexibility: quantification of benefits in the EU, , , , and . Report, smartEn Smart Energy Europe, Brussels, (September 2022)Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition., , , , and . J. Ambient Intell. Smart Environ., 10 (4): 345-357 (2018)Mood classification through physiological parameters., , , , and . J. Ambient Intell. Humaniz. Comput., 12 (4): 4471-4484 (2021)Feasibility Study on the Role of Personality, Emotion, and Engagement in Socially Assistive Robotics: A Cognitive Assessment Scenario., , , , and . Informatics, 8 (2): 23 (2021)Measuring the perception of the personalized activities with CloudIA robot., , , and . CoRR, (2023)Machine Learning based Physical Human-Robot Interaction for Walking Support of Frail People., , , , and . EMBC, page 3404-3407. IEEE, (2019)Feasibility study on the assessment of auditory sustained attention through walking motor parameters in mild cognitive impairments and healthy subjects., , , , , , , and . EMBC, page 897-900. IEEE, (2017)Physiological Wireless Sensor Network for the Detection of Human Moods to Enhance Human-Robot Interaction., , , , , and . ForItAAL, volume 544 of Lecture Notes in Electrical Engineering, page 361-376. Springer, (2018)Unsupervised emotional state classification through physiological parameters for social robotics applications., , , , and . Knowl. Based Syst., (2020)