TL;DR
An innovative framework that integrates WiFi-based Channel State Information with the advanced object recognition power of YOLOv8 to enable robust, contactless activity classification and employs the deep learning capabilities of YOLOv8 for precise identification of diverse indoor actions.
This survey provides a systematic overview of cutting-edge research on Wi-Fi-enabled indoor human activity detection, classify mainstream technologies along three dimensions: signal processing pipelines, learning paradigms, and application granularity, and further dissect core challenges including environmental adaptab...
The proposed UniSense-CSI is a unified multi-task framework that jointly learns dynamic gesture recognition, static posture classification, and fall detection and converts CSI signals into pseudo-RGB images, extracts spatio-temporal features using a customized ConvNeXt backbone, and leverages an improved PerceiverIO mo...
Multi-user WiFi-based human activity recognition (HAR) with channel state information (CSI) is challenging because the received CSI contains overlapping motion-induced channel variations from multiple users, which complicates robust per-user activity inference. In this letter, we propose a motion-aware convolution fram...
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.
LipHS, a lightweight feature extraction framework capable of simultaneously capturing multi-level information from CSI signals, is presented, a lightweight feature extraction framework capable of simultaneously capturing multi-level information from CSI signals that achieves model lightweighting while maintaining robus...
The WiFuse framework is presented, a dual-stream Channel State Information (CSI) framework for human activity recognition (HAR) that pairs denoised time-domain amplitude variations with 2D-FFT-derived Delay-Doppler motion representations computed from the sanitized channel phase to improve recognition performance under...
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