for Human Activity Recognition: Overview, Advances, and Challenges

for Human Activity Recognition: Overview, Advances, and Challenges

for Human Activity Recognition: Overview, Advances, and Challenges

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.

for Human Activity Recognition: Overview, Advances, and Challenges

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.

From Inertial to Ambient: A Systematic Review of Sensor Modalities and Data Acquisition Strategies in Human Activity Recognition

This systematic review provides a comprehensive examination of the full spectrum of sensor modalities employed in HAR research spanning wearable inertial sensors, physiological sensors, vision-based sensors, depth cameras, ambient sensing systems, and emerging multimodal fusion frameworks tracing, technical characteris...

An Effective, Reliable, and Robust Framework for Human Activity Recognition Using Wearable Sensors

Human Activity Recognition (HAR) through wearable sensors greatly improves the quality of human life through its multiple applications. For HAR, multi-sensor channel information is vital for optimal performance. Current work states that applying an attention neural network to prioritize discriminatory sensor channels h...

A Review of TinyML for Human Activity Recognition on Edge Devices

This paper presents a comprehensive review of TinyML for HAR, covering foundational concepts, methodologies, and applications, and critically examined the state of the art of existing works and approaches that combine TinyML and HAR.