MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation
arXiv:2605.01369v2 Announce Type: replace-cross Abstract: Deep learning has been widely adopted for WiFi CSI-based human activity recognition (HAR) due to its ability to learn spatio-temporal features in a privacy-preserving and cost-effective manner. However, DL-based models generalize poorly across environments, a challenge amplified in multi-user settings where overlapping activities cause CSI entanglement and domain shifts. Practical deployments often limit access to labeled source data due to privacy constraints, motivating source-free adaptation using only unlabeled target-domain CSI and
The increasing sophistication of deep learning and the ubiquity of Wi-Fi infrastructure are converging to enable new forms of human sensing.
This research addresses a critical challenge in privacy-preserving human activity recognition, enhancing the reliability and applicability of Wi-Fi sensing in complex, real-world environments.
The ability to accurately monitor human activity in multi-user settings without traditional cameras or wearables opens new avenues for surveillance, health monitoring, and smart environments.
- · Smart home developers
- · Elderly care providers
- · Privacy-focused surveillance companies
- · AI/ML researchers in sensing
- · Traditional camera-based surveillance companies
- · Wearable health tech (partial)
- · Privacy advocates (potential misuse of technology)
More robust and privacy-preserving human activity recognition systems become commercially viable.
Increased adoption of Wi-Fi sensing in smart buildings and healthcare could lead to new regulatory frameworks.
The technology could evolve to enable more granular and predictive behavioral analysis, impacting personal privacy and autonomy.
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Read at arXiv cs.LG