The Wi-Fi sensing technique holds great promise for future smart homes, thanks to the widespread use of Wi-Fi devices. With this technique, we can deduce the behavior of the target based on the channel state information (CSI), which is obtained during Wi-Fi communication. However, existing Wi-Fi sensing technologies are not compatible with standard communication technologies. This is because Wi-Fi sensing usually relies on capturing CSI from high-frequency communication packets, whereas regular IoT communication does not consistently maintain such high communication rates. To achieve precise sensing even with a low packet rate, we introduce LowDetrack, an indoor human detection and tracking system at ultra-low packet rates with Wi-Fi. In particular, we utilize compressed sensing to supplement missing data compared to existing systems that rely on linear interpolation or neural networks. To detect and track the target, our insights are twofold: 1) We combine compressed sensing and Fresnel zone to a theoretical model for accurately obtaining the reflection path change rate, which can be converted into the actual velocity of the target; 2) We investigate the mapping relationship between the dynamic frequency composition ratios in different links, which can provide navigation for velocity direction and correct direction recognition errors. We implement LowDetrack on commercial off-the-shelf Wi-Fi and realize human detection and tracking, where the median tracking error is 0.76m at the packet rate of 25 Hz.

In medical diagnostics, the accurate classification and analysis of biomedical signals play a crucial role, particularly in the diagnosis of neurological disorders such as epilepsy. Electroencephalogram (EEG) signals, which represent the electrical activity of the brain, are fundamental in identifying epileptic seizures. However, challenges such as data scarcity and imbalance significantly hinder the development of robust diagnostic models. Addressing these challenges, in this paper, we explore enhancing medical signal processing and diagnosis, with a focus on epilepsy classification through EEG signals, by harnessing AI-generated content techniques. We introduce a novel framework that utilizes generative adversarial networks for the generation of synthetic EEG signals to augment existing datasets, thereby mitigating issues of data scarcity and imbalance. Furthermore, we incorporate an attention-based temporal convolutional network model to efficiently process and classify EEG signals by emphasizing salient features crucial for accurate diagnosis. Our comprehensive evaluation, including rigorous ablation studies, is conducted on the widely recognized Bonn Epilepsy Data. The results achieves an accuracy of 98.89% and F1 score of 98.91%. The findings demonstrate substantial improvements in epilepsy classification accuracy, showcasing the potential of AI-generated content in advancing the field of medical signal processing and diagnosis.

Location-based services and identification hold promise for future smart home applications. Through them, we can provide customized services for specific users in current locations. Recent studies have demonstrated that Wi-Fi signals can be leveraged to achieve device-free tracking and gait recognition. Despite their good performance, these two technologies are not effectively integrated for the following reasons: First, the device-free tracking method might yield tracking results that conflict with human gait. Second, extracting gait features relies on knowing or accurately estimating the user's trajectory. Consequently, gait recognition and tracking are inherently linked, but there has been no effective approach to integrate these two techniques. In this paper, we present STAGR, a system capable ofSimultaneousTrackingAndGaitRecognition. The main contribution of our technique is that we establish a theoretical model that reveals how to transform path-dependent spectra into path-independent spectra directly. Specifically, we conduct a preliminary study to demonstrate the need for simultaneous tracking and gait recognition. Second, we propose a novel method to extract path-independent gait features, which can significantly save execution time compared with the learning-based method. Third, we design a polar-coordinate filtering method to retain the gait features while correcting the trajectory. We implement a prototype STAGR system and conduct extensive experiments to verify the proposed mechanism. The experimental results show that we can realize simultaneous tracking and gait recognition. The median tracking error is$ 0.45m$, while the recognition accuracy is 95.3% for 6 users.