AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes a room-specialized Mixture-of-Experts (MoE) architecture for edge-based ADL recognition using ambient smart home sensors and establishes a foundation for future edge-native healthcare applications, including continual and federated learning.
A semi-supervised approach that integrates Contrastive Predictive Coding (CPC) with a hybrid BiGRU-Transformer architecture is introduced, thereby enabling comprehensive temporal modeling for human activity recognition in smart-home environments.
This paper introduces a robust Transformer-based architecture designed to capture long-range temporal dependencies in wireless signals and validated using a comprehensive dataset from 86 volunteers, ensuring high generalization capabilities across diverse human motion patterns.
A resource-constrained CNN-GRU hybrid model for HAR on the WISDM dataset that uses convolutional layers for spatial learning and gated recurrent units (GRU) for sequence learning, enabling real-time HAR on edge devices.
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...
Radar-based human activity recognition (HAR) is a promising solution for elderly healthcare because it enables non-contact and privacy-preserving monitoring. However, existing lightweight CNN-based methods often have limited receptive fields, while many transformer-based approaches introduce high computational overhead...
Although further refinement is required to improve fall detection, the proposed approach provides a practical foundation for accessible elderly monitoring and has the potential to support caregivers, independent ageing, and community-oriented healthcare.
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