Year: 2026
Type: book-chapter
Abstract: Human activity recognition (HAR) plays a pivotal role in ambient assisted living, particularly for monitoring the elderly and patients with chronic conditions. However, traditional approaches relying on wearable sensors or video cameras face significant challenges regarding user compliance and privacy intrusion. To mitigate these issues, this paper proposes a device-free sensing (DFS) ( https://github.com/mestrelan/MDA-CSI ) framework utilizing Wi-Fi channel state information (CSI), named . We introduce a robust Transformer-based architecture designed to capture long-range temporal dependencies in wireless signals. was validated using a comprehensive dataset from 86 volunteers, ensuring high generalization capabilities across diverse human motion patterns.
Source: Annals of Telecommunications
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,890
Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Financiadora de Estudos e Projetos, Universidade Federal Fluminense, Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica
Award: G1207375514
OpenAlex ID: W7203445708