Machine learning validation demonstrates accurate device-free activity recognition across 86 volunteers, highlighting privacy-preserving ambient health monitoring through standard Wi-Fi signals.
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.
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Santos et al. (2026) studied this question.
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