This paper provides a comprehensive survey of the integration of FL into HAR applications, providing an in-depth analysis of recent advances and their practical implications, and explores key advances in FL-based HAR methodologies, including model architectures, optimization techniques, and different applications.
This study reviews pre-2018 developments and proposes an improved HAR framework incorporating advanced feature engineering, sensor fusion, and ensemble learning techniques, which shows strong potential in healthcare, fitness, and smart environments.
Silvia Diallo, Fatima Zahra El Idrissi· International Journal of Mod...· 0 citations
- Human Activity Recognition (HAR) is a rapidly advancing research domain with transformative applications across healthcare, smart homes, rehabilitation, elderly monitoring, sports analytics, and context-aware computing. The performance and deployability of HAR systems are fundamentally determined by the sensor modali...
O. A. Ayegbusi, M. Onyesolu· Iconic research and engineer...· 0 citations
The integration of Tiny Machine Learning (TinyML) into human activity recognition (HAR) represents a paradigm shift in artificial intelligence, enabling real-time, efficient, and privacy-preserving analysis on resource-constrained edge devices. This paper presents a comprehensive review of TinyML for HAR, covering foun...
Unknown authors· Machine Learning and Knowled...· 0 citations
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