This paper presents EMGSense, a low-effort self-supervised domain adaptation framework for sensing applications based on Electromyography (EMG). EMGSense addresses one of the fundamental challenges in EMG cross-user sensing—the significant performance degradation caused by time-varying biological heterogeneity—in a low-effort (data-efficient and label-free) manner. To alleviate the burden of data collection and avoid labor-intensive data annotation, we propose two EMG-specific data augmentation methods to simulate the EMG signals generated in various conditions and scope the exploration in label-free scenarios. We model combating biological heterogeneity-caused performance degradation as a multi-source domain adaptation problem that can learn from the diversity among source users to eliminate EMG heterogeneous biological features. To relearn the target-user-specific biological features from the unlabeled data, we integrate advanced self-supervised techniques into a carefully designed deep neural network (DNN) structure. The DNN structure can seamlessly perform two training stages that complement each other to adapt to a new user with satisfactory performance. Comprehensive evaluations on two sizable datasets collected from 13 participants indicate that EMGSense achieves an average accuracy of 91.9% and 81.2% in gesture recognition and activity recognition, respectively. EMGSense outperforms the state-of-the-art EMG-oriented domain adaptation approaches by 12.5%-17.4% and achieves a comparable performance with the one trained in a supervised learning manner.@en

Smart space has emerged as a new paradigm that combines sensing, communication, and artificial intelligence technologies to offer various customized services. A fundamental requirement of these services is person identification. Although a variety of person-identification approaches has been proposed, they suffer from several limitations in practical applications, such as low energy efficiency, accuracy degradation, and privacy issue. This article proposes an energy-harvesting-based privacy-preserving gait recognition scheme for smart space, which is named PrivGait. In PrivGait, we extract discriminative features from 1-D gait signal and design an attention-based long short-term memory (LSTM) network to classify different people. Moreover, we leverage a novel Bloom filter-based privacy-preserving technique to address the privacy leakage problem. To demonstrate the feasibility of PrivGait, we design a proof-of-concept prototype using off-the-shelf energy-harvesting hardware. Extensive evaluation results show that the proposed scheme outperforms state of the art by 6%-10% and incurs low system cost while preserving user's privacy.@en

Radio-frequency (RF) energy harvesting is a promising technology for Internet-of-Things (IoT) devices to power sensors and prolong battery life. In this paper, we present a novel side-channel attack that leverages RF energy harvesting signals to eavesdrop mobile app activities. To demonstrate this novel attack, we propose AppListener, an automated attack framework that recognizes fine-grained mobile app activities from harvested RF energy. The RF energy is harvested from a custom-built RF energy harvester which generates voltage signals from ambient Wi-Fi transmissions, and app activities are recognized from a three-tier classification algorithm. We evaluate AppListener with four mobile devices running 40 common mobile apps (e.g., YouTube, Facebook, and WhatsApp) belonging to five categories (i.e., video, music, social media, communication, and game); each category contains five application-specific activities. Experiment results show that AppListener achieves over 99% accuracy in differentiating four different mobile devices, over 98% accuracy in classifying 40 different apps, and 86.7% accuracy in recognizing five sets of application-specific activities. Moreover, a comprehensive study is conducted to show AppListener is robust to a number of impact factors, such as distance, environment, and non-target connected devices. Practices of integrating AppListener into commercial IoT devices also demonstrate that it is easy to deploy. Finally, countermeasures are presented as the first step to defend against this novel attack.@en