Optimized Edge-Cloud System for Activity Monitoring Using Knowledge Distillation Déniz Cerpa, José Daniel Ros Vidal, Eduardo Martínez Ortigosa, Eva Barranco Expósito, Francisco machine learning internet of things real-time and embedded systems Driven by the increasing care needs of residents in long-term care facilities, Ambient Assisted Living paradigms have become very popular, offering new solutions to alleviate this burden. This work proposes an efficient edge-cloud system for indoor activity monitoring in long-term care institutions. Action recognition from video streams is implemented via Deep Learning networks running at edge nodes. Edge Computing stands out for its power efficiency, reduction in data transmission bandwidth, and inherent protection of residents’ sensitive data. To implement Artificial Intelligence models on these resource-limited edge nodes, complex Deep Learning networks are first distilled. Knowledge distillation allows for more accurate and efficient neural networks, boosting recognition performance of the solution by up to 8% without impacting resource usage. Finally, the central server runs a Quality and Resource Management (QRM) tool that monitors hardware qualities and recognition performance. This QRM tool performs runtime resource load balancing among the local processing devices ensuring real-time operation and optimized energy consumption. Also, the QRM module conducts runtime reconfiguration switching the running neural network to optimize the use of resources at the node and to improve the overall recognition, especially for critical situations such as falls. As part of our contributions, we also release the manually curated Indoor Action Dataset. 2024-12-11T07:25:15Z 2024-12-11T07:25:15Z 2024-12-04 journal article Deniz Cerpa, D. et. al. Electronics 2024, 13(23), 4786. [https://doi.org/10.3390/electronics13234786] https://hdl.handle.net/10481/97862 10.3390/electronics13234786 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI