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This paper presents an energy-aware, privacy-preserving human activity recognition (HAR) framework designed for low-power embedded systems, utilizing a machine learning-based camouflaged signal segmentation technique. The method effectively reduces the inference of sensitive attributes like gender and age while maintaining HAR accuracy, addressing privacy concerns associated with data collection from wearable IoT devices. Experimental evaluations demonstrate the system

Author

IJRES team

Language

EN