MetaGanFi

Abstract

Human has an unique gait and prior works show increasing potentials in using WiFi signals to capture the unique signature of individuals' gait. However, existing WiFi-based human identification (HI) systems have not been ready for real-world deployment due to various strong assumptions including identification of known users and sufficient training data captured in predefined domains such as fixed walking trajectory/orientation, WiFi layout (receivers locations) and multipath environment (deployment time and site). In this paper, we propose a WiFi-based HI system, MetaGanFi, which is able to accurately identify unseen individuals in uncontrolled domain with only one or few samples. To achieve this, the MetaGanFi proposes a domain unification model, CCG-GAN that utilizes a conditional cycle generative adversarial networks to filter out irrelevant perturbations incurred by interfering domains. Moreover, the MetaGanFi proposes a domain-agnostic meta learning model, DA-Meta that could quickly adapt from one/few data samples to accurately recognize unseen individuals. The comprehensive evaluation applied on a real-world dataset show that the MetaGanFi can identify unseen individuals with average accuracies of 87.25% and 93.50% for 1 and 5 available data samples (shot) cases, captured in varying trajectory and multipath environment, 86.84% and 91.25% for 1 and 5-shot cases in varying WiFi layout scenarios, while the overall inference process of domain unification and identification takes about 0.1 second per sample.

Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device

“…In 2016, Zhang et al [11] proposed WiFi-ID, pioneering the integration of WiFi sensing into identity recognition for the first time and leveraging CSI data. Since then, WiFi-based identification systems have undergone extensive development, including non-line-of-sight (NLOS) [12,13], cross-domain [23][24][25], and multi-user [26][27][28] WiFi identification approaches. However, in the aforementioned identification systems, the classifier's classification categories are predetermined.…”

Sensing the care: advancing unobtrusive sensing solutions to support informal caregivers of older adults with cognitive impairment

“…When concerning impact due to persons, seemingly subtle modifications, such as the orientation of persons with respect to the sensing device and variations in activity execution, have been found to influence CSI-based HAR [227] [228]. In addition to persons, various other factors have been identified in the literature that can influence CSI-based HAR, such as hardware specifications [225], the quality of CSI data [334], and the timing of day during monitoring [226] [227]. Many of these factors were present in the realistic dataset (Wi-Gitation) used for exploration in Chapter 4.…”